mirror of
https://github.com/guezoloic/millesima-ai-engine.git
synced 2026-04-02 11:51:36 +00:00
fix(learning.ipynb): correction cross_val_score avec uniquement X_test et y_test
This commit is contained in:
@@ -2,10 +2,19 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 12,
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"id": "faafb9a0",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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@@ -101,7 +110,7 @@
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 13,
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"id": "8342340f",
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"metadata": {},
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"outputs": [],
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@@ -126,7 +135,7 @@
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"cell_type": "code",
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"execution_count": 14,
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"id": "9dfdc01f",
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@@ -175,7 +184,7 @@
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 15,
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"id": "99de3ed7",
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"metadata": {},
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@@ -208,7 +217,7 @@
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 16,
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"id": "09eca16d",
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"metadata": {},
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"outputs": [
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@@ -246,7 +255,7 @@
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 17,
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"id": "b94a89f2",
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"metadata": {},
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"outputs": [
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@@ -323,7 +332,7 @@
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 18,
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"id": "4f1c169f",
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"metadata": {},
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"outputs": [
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@@ -360,7 +369,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 19,
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"id": "91cedffb",
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"metadata": {},
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"outputs": [
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@@ -457,7 +466,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 20,
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"id": "4c21cd56",
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"metadata": {},
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"outputs": [
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@@ -473,9 +482,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|AD|0.393010|\n",
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"|Normalisation + AD|0.393010|\n",
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"|Standardisation + AD|0.393034|\n"
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"|AD|0.386319|\n",
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"|Normalisation + AD|0.386319|\n",
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"|Standardisation + AD|0.386319|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -496,9 +505,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|AD|0.403744|\n",
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"|Normalisation + AD|0.403744|\n",
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"|Standardisation + AD|0.403761|\n"
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"|AD|0.382689|\n",
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"|Normalisation + AD|0.382695|\n",
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"|Standardisation + AD|0.382691|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -519,9 +528,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|AD|0.400791|\n",
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"|Normalisation + AD|0.400791|\n",
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"|Standardisation + AD|0.400821|\n"
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"|AD|0.371764|\n",
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"|Normalisation + AD|0.374744|\n",
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"|Standardisation + AD|0.371729|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -534,7 +543,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best score= 0.40376125774243815 depth= 4 method= Standardisation + AD\n"
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"best score= 0.3863187373819251 depth= 3 method= AD\n"
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]
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}
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],
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@@ -562,7 +571,7 @@
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" else make_pipeline(scaler(), DecisionTreeRegressor(max_depth=depth))\n",
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" )\n",
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" model.fit(X_train, y_train)\n",
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" score: float = cross_val_score(model, X, y_log, cv=5).mean()\n",
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" score: float = cross_val_score(model, X_test, y_test, cv=5).mean()\n",
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" ad_table.ajoutligne(f\"{name}\", score)\n",
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"\n",
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" if score > best_score_ad:\n",
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@@ -616,7 +625,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 21,
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"id": "f72f499f",
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"metadata": {},
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"outputs": [
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@@ -632,9 +641,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|KNN|0.371305|\n",
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"|Normalisation + KNN|0.392151|\n",
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"|Standardisation + KNN|0.392397|\n"
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"|KNN|0.370389|\n",
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"|Normalisation + KNN|0.341947|\n",
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"|Standardisation + KNN|0.369662|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -655,9 +664,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|KNN|0.391092|\n",
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"|Normalisation + KNN|0.412238|\n",
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"|Standardisation + KNN|0.402197|\n"
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"|KNN|0.390801|\n",
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"|Normalisation + KNN|0.349482|\n",
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"|Standardisation + KNN|0.381631|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -670,7 +679,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best score= 0.4122380532367001 neighbor= 5 method= Normalisation + KNN\n"
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"best score= 0.39080066451618123 neighbor= 5 method= KNN\n"
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]
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}
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],
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@@ -698,7 +707,7 @@
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" else make_pipeline(scaler(), KNeighborsRegressor(n_neighbors=n))\n",
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" )\n",
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" model.fit(X_train, y_train)\n",
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" score: float = cross_val_score(model, X, y_log, cv=5).mean()\n",
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" score: float = cross_val_score(model, X_test, y_test, cv=5).mean()\n",
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" knn_table.ajoutligne(f\"{name}\", score)\n",
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"\n",
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" if score > best_score_ad:\n",
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@@ -740,7 +749,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 22,
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"id": "9f764f3a",
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"metadata": {},
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"outputs": [
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@@ -750,8 +759,8 @@
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|LR|0.452908|\n",
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"|AD|0.403761|\n",
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"|KNN|0.412238|\n"
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"|AD|0.386319|\n",
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"|KNN|0.390801|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -804,7 +813,7 @@
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 23,
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"id": "9084e87e",
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"metadata": {},
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"outputs": [
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@@ -841,7 +850,7 @@
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"cell_type": "code",
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"execution_count": 24,
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"id": "fdcdfb17",
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"metadata": {},
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"outputs": [
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@@ -851,7 +860,7 @@
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"0.24194691318111572"
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]
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},
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"execution_count": 13,
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -893,7 +902,7 @@
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"execution_count": 25,
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"id": "c4f6c27f",
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"metadata": {},
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"outputs": [
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@@ -903,7 +912,7 @@
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"<Axes: >"
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]
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},
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"execution_count": 14,
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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@@ -938,7 +947,7 @@
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"cell_type": "code",
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"execution_count": 15,
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"id": "2e512052",
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"metadata": {},
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"outputs": [
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@@ -1125,7 +1134,7 @@
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"App_Haut-Médoc -0.070383"
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]
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},
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"execution_count": 15,
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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@@ -1146,7 +1155,7 @@
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 27,
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"id": "4010aa12",
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"metadata": {},
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"outputs": [
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@@ -1156,7 +1165,7 @@
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"0.24551258031003886"
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]
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},
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"execution_count": 16,
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -1204,7 +1213,7 @@
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 28,
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"id": "1510f763",
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"metadata": {},
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"outputs": [
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@@ -1213,9 +1222,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|Random Forest|0.493344|\n",
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"|Normalisation + RF|0.493113|\n",
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"|Standardisation + RF|0.493782|\n"
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"|Random Forest|0.492498|\n",
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"|Normalisation + RF|0.492721|\n",
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"|Standardisation + RF|0.500235|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -1279,7 +1288,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.20"
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"version": "3.14.3"
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}
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},
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"nbformat": 4,
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