{
 "cells": [
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 73,
   "id": "29e20c6b",
=======
   "execution_count": 2,
   "id": "de4f7f4b",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
<<<<<<< HEAD
    "from sklearn.svm import LinearSVC"
=======
    "from sklearn.svm import LinearSVC\n",
    "import matplotlib.pyplot as plt"
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 74,
   "id": "67b3d7af",
=======
   "execution_count": 3,
   "id": "88b3d717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3a)\n",
    "X = np.linspace(0, 2)\n",
    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
    "X = np.sort(np.random.rand(50)*2)\n",
    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
    "plt.plot(X, T, '.')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('t')\n",
    "plt.show()\n",
    "# Es gibt 50 Merkmale in den Daten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b251a2c1",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
<<<<<<< HEAD
      "[[5.1 3.5 1.4 0.2 0. ]\n",
      " [4.9 3.  1.4 0.2 0. ]\n",
      " [4.7 3.2 1.3 0.2 0. ]\n",
      " [4.6 3.1 1.5 0.2 0. ]\n",
      " [5.  3.6 1.4 0.2 0. ]]\n"
     ]
    }
   ],
   "source": [
    "# 1a) Ja stehen in der richtigen Dimension\n",
    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
    "print(iris_data[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c9c0fd1e",
   "metadata": {},
   "outputs": [
=======
      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
      " 1.93126407 1.9398197 ]\n",
      "[[0.04116899]\n",
      " [0.06877704]\n",
      " [0.09290083]\n",
      " [0.11616722]\n",
      " [0.13010319]\n",
      " [0.19534423]\n",
      " [0.24407647]\n",
      " [0.27898772]\n",
      " [0.31198904]\n",
      " [0.31203728]\n",
      " [0.34104825]\n",
      " [0.36364993]\n",
      " [0.36680902]\n",
      " [0.36970891]\n",
      " [0.39934756]\n",
      " [0.42467822]\n",
      " [0.51755996]\n",
      " [0.58245828]\n",
      " [0.5842893 ]\n",
      " [0.60848449]\n",
      " [0.60922754]\n",
      " [0.62342215]\n",
      " [0.73272369]\n",
      " [0.74908024]\n",
      " [0.86389004]\n",
      " [0.88030499]\n",
      " [0.91213997]\n",
      " [0.99035382]\n",
      " [1.02846888]\n",
      " [1.04013604]\n",
      " [1.04951286]\n",
      " [1.09342056]\n",
      " [1.18482914]\n",
      " [1.19731697]\n",
      " [1.20223002]\n",
      " [1.2150897 ]\n",
      " [1.22370579]\n",
      " [1.32504457]\n",
      " [1.36846605]\n",
      " [1.41614516]\n",
      " [1.46398788]\n",
      " [1.57035192]\n",
      " [1.6167947 ]\n",
      " [1.66488528]\n",
      " [1.73235229]\n",
      " [1.8186408 ]\n",
      " [1.89777107]\n",
      " [1.90142861]\n",
      " [1.93126407]\n",
      " [1.9398197 ]]\n"
     ]
    },
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "    border: none;\n",
       "    padding: 0;\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".copy-paste-icon:focus-visible {\n",
       "    outline: 1px solid currentColor;\n",
       "    outline-offset: 1px;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
<<<<<<< HEAD
       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
=======
       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
<<<<<<< HEAD
       "                 aria-label=\"Copy penalty to clipboard\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
=======
       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
<<<<<<< HEAD
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
=======
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
<<<<<<< HEAD
       "                 aria-label=\"Copy loss to clipboard\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
=======
       "                 aria-label=\"Copy copy_X to clipboard\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
<<<<<<< HEAD
       "            style=\"anchor-name: --doc-link-loss;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
       "            loss\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-loss;\">\n",
       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy dual to clipboard\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
=======
       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
       "            copy_X\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy tol to clipboard\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
<<<<<<< HEAD
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
=======
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1e-06</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
<<<<<<< HEAD
       "                 aria-label=\"Copy C to clipboard\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
=======
       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
<<<<<<< HEAD
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy multi_class to clipboard\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
       "            multi_class\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy class_weight to clipboard\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
=======
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
<<<<<<< HEAD
       "                 aria-label=\"Copy verbose to clipboard\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
=======
       "                 aria-label=\"Copy positive to clipboard\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
<<<<<<< HEAD
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy random_state to clipboard\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy max_iter to clipboard\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000</td>\n",
=======
       "            style=\"anchor-name: --doc-link-positive;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
       "            positive\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-positive;\">\n",
       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
<<<<<<< HEAD
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[0.,1.,2.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
       "           <td>[[-1.54, 2.28],\n",
       " [ 0.1 ,-0.98],\n",
       " [ 0.66,-0.35]]</td>\n",
=======
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[1.64]</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
<<<<<<< HEAD
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
=======
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>-0.1418</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
<<<<<<< HEAD
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
=======
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
<<<<<<< HEAD
       "           <td>2</td>\n",
=======
       "           <td>1</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
<<<<<<< HEAD
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>8</td>\n",
=======
       "            style=\"anchor-name: --doc-link-rank_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
       "            rank_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-rank_;\">\n",
       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int64</td>\n",
       "           <td>np.int64(1)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-singular_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
       "            singular_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-singular_;\">\n",
       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[4.04]</td>\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
       " SPDX-License-Identifier: BSD-3-Clause\n",
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       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
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       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
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       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
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       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
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       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "\n",
       "    const parent = element.parentElement;\n",
       "    if (!parent || !parent.nextElementSibling) {\n",
       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
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       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
<<<<<<< HEAD
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
=======
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
<<<<<<< HEAD
       "forceTheme('sk-container-id-13');</script></body>"
      ],
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 75,
=======
       "forceTheme('sk-container-id-2');</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
<<<<<<< HEAD
    "# 1b)\n",
    "X = iris_data[:,[0,1]]\n",
    "T = iris_data[:,4]\n",
    "clf.fit(X, T)"
=======
    "# 3b)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "print(X)\n",
    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
    "reg.fit(X.reshape(-1,1),T)\n"
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 76,
   "id": "cc181a3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.0 Prozent wurden richtig vorausgesagt\n"
     ]
    }
   ],
   "source": [
    "# 1 c+d)\n",
    "pred = clf.predict(X)\n",
    "correct_perc = (sum(pred == T)/len(pred))*100\n",
    "\n",
    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "16af32f1",
=======
   "execution_count": 17,
   "id": "98476c93",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1e)\n",
    "import nnwplot\n",
    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f4bcee12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1f)\n",
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(X, T)\n",
    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2c8991de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1a5e84f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1h)\n",
    "X_0_3 = iris_data[:,[0,3]]\n",
    "clf.fit(X_0_3, T)\n",
    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "892baf25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_0_3 = iris_data[:,[0,3]]\n",
    "svc.fit(X_0_3, T)\n",
    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1bad3e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
=======
      "image/png": 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",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
<<<<<<< HEAD
     "data": {
      "image/png": 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",
=======
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
      "  3.02194413  3.03595981]\n"
     ]
    }
   ],
   "source": [
    "# 3c)\n",
    "plt.plot(X, T, '.')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('t')\n",
    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
    "plt.show()\n",
    "print(reg.predict(X.reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94ab4bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
<<<<<<< HEAD
    "# 1i)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knc = KNeighborsClassifier()\n",
    "knc.fit(X,T)\n",
    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
    "knc.fit(X_0_3,T)\n",
    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
    "\n"
=======
    "# 3d) Decision Tree Regressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "dtr = DecisionTreeRegressor()\n",
    "dtr.fit(X.reshape(-1,1),T)\n",
    "\n",
    "plt.plot(X, T, '.')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('t')\n",
    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
    "plt.show()"
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 101,
   "id": "d79741d2",
=======
   "execution_count": 20,
   "id": "475aa6e4",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2a) probabilistisches Verfahren\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "X_0_2 = iris_data[:,[0,2]]\n",
    "km = KMeans(4)\n",
    "km.fit(X_0_2)\n",
    "km.predict(X_0_2)\n"
=======
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3d) SV Regressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "svr = SVR()\n",
    "svr.fit(X.reshape(-1,1),T)\n",
    "\n",
    "plt.plot(X, T, '.')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('t')\n",
    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
    "plt.show()"
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
<<<<<<< HEAD
   "display_name": "nnw (3.14.0)",
=======
   "display_name": "nnw (3.14.8)",
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
<<<<<<< HEAD
   "version": "3.14.0"
=======
   "version": "3.14.8"
>>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}