From 8074c95720746dc3f6b334f2b4b4f2b752e33df4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20GUEZO?= Date: Wed, 1 Apr 2026 15:30:50 +0200 Subject: [PATCH] fix(learning.ipynb): correction cross_val_score avec uniquement X_test et y_test --- docs/learning.ipynb | 103 ++++++++++++++++++++++++-------------------- 1 file changed, 56 insertions(+), 47 deletions(-) diff --git a/docs/learning.ipynb b/docs/learning.ipynb index 7294743..1fbd782 100644 --- a/docs/learning.ipynb +++ b/docs/learning.ipynb @@ -2,10 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "id": "faafb9a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -101,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "id": "8342340f", "metadata": {}, "outputs": [], @@ -126,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "id": "9dfdc01f", "metadata": {}, "outputs": [], @@ -175,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "id": "99de3ed7", "metadata": {}, "outputs": [ @@ -208,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "id": "09eca16d", "metadata": {}, "outputs": [ @@ -246,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "id": "b94a89f2", "metadata": {}, "outputs": [ @@ -323,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "id": "4f1c169f", "metadata": {}, "outputs": [ @@ -360,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "id": "91cedffb", "metadata": {}, "outputs": [ @@ -457,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "id": "4c21cd56", "metadata": {}, "outputs": [ @@ -473,9 +482,9 @@ "text/markdown": [ "| Méthode | R² |\n", "| :---: | :---: |\n", - "|AD|0.393010|\n", - "|Normalisation + AD|0.393010|\n", - "|Standardisation + AD|0.393034|\n" + "|AD|0.386319|\n", + "|Normalisation + AD|0.386319|\n", + "|Standardisation + AD|0.386319|\n" ], "text/plain": [ "" @@ -496,9 +505,9 @@ "text/markdown": [ "| Méthode | R² |\n", "| :---: | :---: |\n", - "|AD|0.403744|\n", - "|Normalisation + AD|0.403744|\n", - "|Standardisation + AD|0.403761|\n" + "|AD|0.382689|\n", + "|Normalisation + AD|0.382695|\n", + "|Standardisation + AD|0.382691|\n" ], "text/plain": [ "" @@ -519,9 +528,9 @@ "text/markdown": [ "| Méthode | R² |\n", "| :---: | :---: |\n", - "|AD|0.400791|\n", - "|Normalisation + AD|0.400791|\n", - "|Standardisation + AD|0.400821|\n" + "|AD|0.371764|\n", + "|Normalisation + AD|0.374744|\n", + "|Standardisation + AD|0.371729|\n" ], "text/plain": [ "" @@ -534,7 +543,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "best score= 0.40376125774243815 depth= 4 method= Standardisation + AD\n" + "best score= 0.3863187373819251 depth= 3 method= AD\n" ] } ], @@ -562,7 +571,7 @@ " else make_pipeline(scaler(), DecisionTreeRegressor(max_depth=depth))\n", " )\n", " model.fit(X_train, y_train)\n", - " score: float = cross_val_score(model, X, y_log, cv=5).mean()\n", + " score: float = cross_val_score(model, X_test, y_test, cv=5).mean()\n", " ad_table.ajoutligne(f\"{name}\", score)\n", "\n", " if score > best_score_ad:\n", @@ -616,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "id": "f72f499f", "metadata": {}, "outputs": [ @@ -632,9 +641,9 @@ "text/markdown": [ "| Méthode | R² |\n", "| :---: | :---: |\n", - "|KNN|0.371305|\n", - "|Normalisation + KNN|0.392151|\n", - "|Standardisation + KNN|0.392397|\n" + "|KNN|0.370389|\n", + "|Normalisation + KNN|0.341947|\n", + "|Standardisation + KNN|0.369662|\n" ], "text/plain": [ "" @@ -655,9 +664,9 @@ "text/markdown": [ "| Méthode | R² |\n", "| :---: | :---: |\n", - "|KNN|0.391092|\n", - "|Normalisation + KNN|0.412238|\n", - "|Standardisation + KNN|0.402197|\n" + "|KNN|0.390801|\n", + "|Normalisation + KNN|0.349482|\n", + "|Standardisation + KNN|0.381631|\n" ], "text/plain": [ "" @@ -670,7 +679,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "best score= 0.4122380532367001 neighbor= 5 method= Normalisation + KNN\n" + "best score= 0.39080066451618123 neighbor= 5 method= KNN\n" ] } ], @@ -698,7 +707,7 @@ " else make_pipeline(scaler(), KNeighborsRegressor(n_neighbors=n))\n", " )\n", " model.fit(X_train, y_train)\n", - " score: float = cross_val_score(model, X, y_log, cv=5).mean()\n", + " score: float = cross_val_score(model, X_test, y_test, cv=5).mean()\n", " knn_table.ajoutligne(f\"{name}\", score)\n", "\n", " if score > best_score_ad:\n", @@ -740,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 22, "id": "9f764f3a", "metadata": {}, "outputs": [ @@ -750,8 +759,8 @@ "| Méthode | R² |\n", "| :---: | :---: |\n", "|LR|0.452908|\n", - "|AD|0.403761|\n", - "|KNN|0.412238|\n" + "|AD|0.386319|\n", + "|KNN|0.390801|\n" ], "text/plain": [ "" @@ -804,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "id": "9084e87e", "metadata": {}, "outputs": [ @@ -841,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 24, "id": "fdcdfb17", "metadata": {}, "outputs": [ @@ -851,7 +860,7 @@ "0.24194691318111572" ] }, - "execution_count": 13, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -893,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 25, "id": "c4f6c27f", "metadata": {}, "outputs": [ @@ -903,7 +912,7 @@ "" ] }, - "execution_count": 14, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, @@ -938,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "id": "2e512052", "metadata": {}, "outputs": [ @@ -1125,7 +1134,7 @@ "App_Haut-Médoc -0.070383" ] }, - "execution_count": 15, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1146,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "id": "4010aa12", "metadata": {}, "outputs": [ @@ -1156,7 +1165,7 @@ "0.24551258031003886" ] }, - "execution_count": 16, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1204,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "id": "1510f763", "metadata": {}, "outputs": [ @@ -1213,9 +1222,9 @@ "text/markdown": [ "| Méthode | R² |\n", "| :---: | :---: |\n", - "|Random Forest|0.493344|\n", - "|Normalisation + RF|0.493113|\n", - "|Standardisation + RF|0.493782|\n" + "|Random Forest|0.492498|\n", + "|Normalisation + RF|0.492721|\n", + "|Standardisation + RF|0.500235|\n" ], "text/plain": [ "" @@ -1279,7 +1288,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.20" + "version": "3.14.3" } }, "nbformat": 4,