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added f1 score

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Felix Jan Michael Mucha 2024-06-12 17:19:27 +02:00
parent 06b862b1a1
commit 2f89518bbf
2 changed files with 85 additions and 52 deletions
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74
notebooks/ml_grad_boost_tree.ipynb
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63
notebooks/ml_xgboost.ipynb
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@ -9,7 +9,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -20,10 +20,9 @@
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import xgboost as xgb\n", "import xgboost as xgb\n",
"from sklearn.model_selection import GridSearchCV\n", "from sklearn.model_selection import GridSearchCV\n",
"from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import confusion_matrix, f1_score\n",
"from sklearn.impute import SimpleImputer\n", "import seaborn as sns\n",
"from sklearn.preprocessing import MinMaxScaler\n", "import numpy as np"
"import seaborn as sns"
] ]
}, },
{ {
@ -35,7 +34,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -59,7 +58,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -337,7 +336,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -362,14 +361,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[20:16:51] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n", "[16:58:49] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n",
"Parameters: { \"best_iteration\", \"best_ntree_limit\", \"scikit_learn\" } might not be used.\n", "Parameters: { \"best_iteration\", \"best_ntree_limit\", \"scikit_learn\" } might not be used.\n",
"\n", "\n",
" This could be a false alarm, with some parameters getting used by language bindings but\n", " This could be a false alarm, with some parameters getting used by language bindings but\n",
@ -377,13 +376,7 @@
" but getting flagged wrongly here. Please open an issue if you find any such cases.\n", " but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
"\n", "\n",
"\n", "\n",
"[0]\ttrain-merror:0.16762\teval-merror:0.22603\n" "[0]\ttrain-merror:0.16762\teval-merror:0.22603\n",
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1]\ttrain-merror:0.15220\teval-merror:0.22374\n", "[1]\ttrain-merror:0.15220\teval-merror:0.22374\n",
"[2]\ttrain-merror:0.13849\teval-merror:0.21461\n", "[2]\ttrain-merror:0.13849\teval-merror:0.21461\n",
"[3]\ttrain-merror:0.13535\teval-merror:0.20776\n", "[3]\ttrain-merror:0.13535\teval-merror:0.20776\n",
@ -483,8 +476,8 @@
"[97]\ttrain-merror:0.00029\teval-merror:0.18265\n", "[97]\ttrain-merror:0.00029\teval-merror:0.18265\n",
"[98]\ttrain-merror:0.00029\teval-merror:0.18265\n", "[98]\ttrain-merror:0.00029\teval-merror:0.18265\n",
"[99]\ttrain-merror:0.00029\teval-merror:0.18265\n", "[99]\ttrain-merror:0.00029\teval-merror:0.18265\n",
"CPU times: total: 17.6 s\n", "CPU times: total: 14.3 s\n",
"Wall time: 1.36 s\n" "Wall time: 1.22 s\n"
] ]
} }
], ],
@ -506,7 +499,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 45, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -546,7 +539,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 46, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -577,7 +570,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -609,7 +602,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -618,7 +611,7 @@
"<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>" "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
] ]
}, },
"execution_count": 19, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
@ -640,7 +633,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -679,6 +672,26 @@
"plt.tight_layout()\n", "plt.tight_layout()\n",
"plt.show()" "plt.show()"
] ]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"F1 Score: 0.8157211953487169\n"
]
}
],
"source": [
"# Calculate F1 Score for multiclass classification\n",
"f1 = f1_score(test_y, preds, average='macro')\n",
"\n",
"print('F1 Score:', f1)"
]
} }
], ],
"metadata": { "metadata": {