mirror of
https://github.com/guezoloic/millesima_projetS6.git
synced 2026-04-02 10:21:33 +00:00
fix(learning.ipynb): cross_val_score erreur ajout X_train
This commit is contained in:
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 31,
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"id": "faafb9a0",
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"metadata": {},
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"outputs": [
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@@ -110,7 +110,7 @@
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"cell_type": "code",
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@@ -135,7 +135,7 @@
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 33,
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@@ -184,7 +184,7 @@
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 34,
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"id": "99de3ed7",
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@@ -217,7 +217,7 @@
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 35,
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"id": "09eca16d",
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"metadata": {},
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@@ -255,7 +255,7 @@
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 36,
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"id": "b94a89f2",
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"metadata": {},
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@@ -332,7 +332,7 @@
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 37,
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"id": "4f1c169f",
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"metadata": {},
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"outputs": [
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@@ -369,7 +369,7 @@
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 38,
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"id": "91cedffb",
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"metadata": {},
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"outputs": [
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@@ -466,7 +466,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 39,
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"id": "4c21cd56",
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"metadata": {},
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"outputs": [
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@@ -482,9 +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|0.386319|\n",
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"|Normalisation + AD|0.386319|\n",
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"|Standardisation + AD|0.386319|\n"
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"|AD|0.483688|\n",
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"|Normalisation + AD|0.483688|\n",
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"|Standardisation + AD|0.483719|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -505,9 +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|0.382689|\n",
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"|Normalisation + AD|0.382695|\n",
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"|Standardisation + AD|0.382691|\n"
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"|AD|0.501187|\n",
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"|Normalisation + AD|0.501187|\n",
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"|Standardisation + AD|0.501185|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -528,9 +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|0.371764|\n",
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"|Normalisation + AD|0.374744|\n",
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"|Standardisation + AD|0.371729|\n"
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"|AD|0.511089|\n",
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"|Normalisation + AD|0.508518|\n",
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"|Standardisation + AD|0.512649|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -543,7 +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= 0.3863187373819251 depth= 3 method= AD\n"
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"best score= 0.512649496806262 depth= 5 method= Standardisation + AD\n"
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]
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}
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],
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@@ -571,7 +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_test, y_test, cv=5).mean()\n",
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" score: float = cross_val_score(model, X_train, y_train, 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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@@ -625,7 +625,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 40,
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"id": "f72f499f",
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"metadata": {},
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"outputs": [
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@@ -641,9 +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|0.370389|\n",
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"|Normalisation + KNN|0.341947|\n",
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"|Standardisation + KNN|0.369662|\n"
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"|KNN|0.485764|\n",
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"|Normalisation + KNN|0.497729|\n",
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"|Standardisation + KNN|0.489906|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -664,9 +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|0.390801|\n",
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"|Normalisation + KNN|0.349482|\n",
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"|Standardisation + KNN|0.381631|\n"
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"|KNN|0.504888|\n",
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"|Normalisation + KNN|0.500504|\n",
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"|Standardisation + KNN|0.493475|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -679,7 +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= 0.39080066451618123 neighbor= 5 method= KNN\n"
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"best score= 0.5048884037669835 neighbor= 5 method= KNN\n"
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]
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}
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],
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@@ -707,10 +707,10 @@
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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_test, y_test, cv=5).mean()\n",
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" score: float = cross_val_score(model, X_train, y_train, 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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" if score > best_score_knn:\n",
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" best_score_knn = score\n",
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" best_neighbor = n\n",
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" best_scaler_name = name\n",
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@@ -749,7 +749,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 41,
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"id": "9f764f3a",
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"metadata": {},
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"outputs": [
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@@ -759,8 +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|0.386319|\n",
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"|KNN|0.390801|\n"
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"|AD|0.512649|\n",
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"|KNN|0.504888|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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@@ -773,7 +773,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best_model= LR best_scaler= MinMaxScaler\n"
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"best_model= AD best_scaler= StandardScaler\n"
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]
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}
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],
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@@ -813,7 +813,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 42,
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"id": "9084e87e",
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"metadata": {},
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"outputs": [
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@@ -850,17 +850,17 @@
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 43,
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"id": "fdcdfb17",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.24194691318111572"
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"0.3938363073944231"
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]
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},
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"execution_count": 24,
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"execution_count": 43,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -902,7 +902,7 @@
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 44,
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"id": "c4f6c27f",
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"metadata": {},
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"outputs": [
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@@ -912,7 +912,7 @@
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"<Axes: >"
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]
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},
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"execution_count": 25,
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"execution_count": 44,
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"metadata": {},
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@@ -947,7 +947,7 @@
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 45,
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"id": "2e512052",
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"metadata": {},
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"outputs": [
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@@ -1134,7 +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": 26,
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"execution_count": 45,
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"metadata": {},
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"output_type": "execute_result"
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@@ -1155,17 +1155,17 @@
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 46,
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"id": "4010aa12",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.24551258031003886"
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"0.4489472637581393"
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]
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},
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"execution_count": 27,
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"execution_count": 46,
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"metadata": {},
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"output_type": "execute_result"
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@@ -1213,7 +1213,7 @@
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 47,
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"id": "1510f763",
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"metadata": {},
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"outputs": [
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@@ -1222,9 +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|0.492498|\n",
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"|Normalisation + RF|0.492721|\n",
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"|Standardisation + RF|0.500235|\n"
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"|Random Forest|0.496806|\n",
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"|Normalisation + RF|0.494283|\n",
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"|Standardisation + RF|0.486890|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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