diff --git a/Lösungen_N/AB2.ipynb b/Lösungen_N/AB2.ipynb
new file mode 100644
index 0000000..df84f5a
--- /dev/null
+++ b/Lösungen_N/AB2.ipynb
@@ -0,0 +1,1356 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "de4f7f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.svm import LinearSVC\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[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",
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+      " [0.60848449]\n",
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+      " [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"
+     ]
+    },
+    {
+     "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",
+       "</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",
+       "        <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",
+       "                 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.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",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy copy_X to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\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",
+       "        </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",
+       "            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",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\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",
+       "        </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 positive to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\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",
+       "        </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",
+       "            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",
+       "\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",
+       "            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",
+       "\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",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
+       "            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",
+       "           <td>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-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",
+       "\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",
+       "*/\n",
+       "\n",
+       "function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    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",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    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",
+       "    var content = element.closest('.features').querySelector('tbody')\n",
+       "                  .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",
+       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+       "    const originalStyle = element.style;\n",
+       "    const copyMark = document.createElement('span');\n",
+       "    copyMark.innerHTML = '✔';\n",
+       "    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",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "98476c93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "475aa6e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nnw (3.14.8)",
+   "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",
+   "version": "3.14.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}