mirror of
https://github.com/guezoloic/millesima_projetS6.git
synced 2026-04-02 12:01:40 +00:00
fix(clearning.ipynb): changement cross_val_score en score
This commit is contained in:
@@ -2,19 +2,10 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 1,
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"id": "faafb9a0",
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"metadata": {},
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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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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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@@ -110,7 +101,7 @@
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"cell_type": "code",
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"execution_count": 32,
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@@ -135,7 +126,7 @@
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@@ -184,7 +175,7 @@
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"execution_count": 34,
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"execution_count": 4,
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"id": "99de3ed7",
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@@ -217,7 +208,7 @@
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 5,
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"id": "09eca16d",
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@@ -255,7 +246,7 @@
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 6,
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"id": "b94a89f2",
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@@ -332,7 +323,7 @@
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 7,
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"id": "4f1c169f",
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@@ -369,7 +360,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 8,
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"id": "91cedffb",
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"metadata": {},
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"outputs": [
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@@ -466,7 +457,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 9,
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"id": "4c21cd56",
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"metadata": {},
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"outputs": [
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@@ -528,9 +519,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|AD|0.511089|\n",
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"|Normalisation + AD|0.508518|\n",
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"|Standardisation + AD|0.512649|\n"
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"|AD|0.510519|\n",
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"|Normalisation + AD|0.508927|\n",
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"|Standardisation + AD|0.506827|\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 +534,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best score= 0.512649496806262 depth= 5 method= Standardisation + AD\n"
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"best score= 0.5105192880780061 depth= 5 method= AD\n"
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]
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}
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],
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@@ -614,6 +605,14 @@
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"Refaites l’expérience avec un paramètre n_neigbors=5. L’augmentation du nombre de voisins considérés permet-elle de meilleurs résultats ? Si cette amélioration n’est pas significative, nous conserverons n_neighbors = 4 pour la suite, par souci de simplicité."
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]
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},
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{
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"cell_type": "markdown",
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"id": "88185c62",
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"metadata": {},
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"source": [
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"Aucune amélioration signification par rapport au pré et post traitement"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72869cf6",
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@@ -625,7 +624,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 10,
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"id": "f72f499f",
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"metadata": {},
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"outputs": [
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@@ -641,9 +640,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|KNN|0.485764|\n",
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"|Normalisation + KNN|0.497729|\n",
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"|Standardisation + KNN|0.489906|\n"
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"|KNN|0.463459|\n",
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"|Normalisation + KNN|0.465584|\n",
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"|Standardisation + KNN|0.480575|\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 +663,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|KNN|0.504888|\n",
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"|Normalisation + KNN|0.500504|\n",
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"|Standardisation + KNN|0.493475|\n"
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"|KNN|0.492485|\n",
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"|Normalisation + KNN|0.485298|\n",
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"|Standardisation + KNN|0.495728|\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 +678,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best score= 0.5048884037669835 neighbor= 5 method= KNN\n"
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"best score= 0.49572822708745434 neighbor= 5 method= Standardisation + KNN\n"
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]
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}
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],
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@@ -707,7 +706,7 @@
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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_train, y_train, cv=5).mean()\n",
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" score: float = model.score(X_test, y_test)\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_knn:\n",
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@@ -749,7 +748,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 11,
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"id": "9f764f3a",
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"metadata": {},
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"outputs": [
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@@ -759,8 +758,8 @@
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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|0.512649|\n",
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"|KNN|0.504888|\n"
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"|AD|0.510519|\n",
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"|KNN|0.495728|\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 +772,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best_model= AD best_scaler= StandardScaler\n"
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"best_model= AD best_scaler= DecisionTreeRegressor\n"
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]
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}
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],
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@@ -813,7 +812,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 12,
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"id": "9084e87e",
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"metadata": {},
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"outputs": [
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@@ -850,17 +849,17 @@
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 13,
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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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"0.3938363073944231"
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"0.39383630739442343"
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]
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},
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"execution_count": 43,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -902,7 +901,7 @@
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"metadata": {},
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"outputs": [
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@@ -912,7 +911,7 @@
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"<Axes: >"
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]
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},
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"execution_count": 44,
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"execution_count": 14,
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"metadata": {},
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@@ -947,7 +946,7 @@
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"metadata": {},
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"outputs": [
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@@ -1134,7 +1133,7 @@
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"App_Haut-Médoc -0.070383"
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]
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},
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"execution_count": 45,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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@@ -1155,7 +1154,7 @@
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"id": "4010aa12",
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"metadata": {},
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"outputs": [
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@@ -1165,7 +1164,7 @@
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"0.4489472637581393"
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]
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},
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"execution_count": 46,
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"execution_count": 16,
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"metadata": {},
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@@ -1213,7 +1212,7 @@
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 17,
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"id": "1510f763",
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"metadata": {},
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"outputs": [
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@@ -1222,9 +1221,9 @@
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"text/markdown": [
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"| Méthode | R² |\n",
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"| :---: | :---: |\n",
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"|Random Forest|0.496806|\n",
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"|Normalisation + RF|0.494283|\n",
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"|Standardisation + RF|0.486890|\n"
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"|Random Forest|0.492518|\n",
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"|Normalisation + RF|0.491729|\n",
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"|Standardisation + RF|0.483625|\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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