Commit 93940d9a by Sylvain Marchienne

### Merge branch 'TP2_mardi' of https://gitlab.utc.fr/DataVenture/api-h19 into TP2_mardi

parents 26de8ff6 36bbb85b
 ... ... @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ "# Regression\n", "Rappel problème de régression" "# TP Apprentissage supervisé: Régression\n", "Dans ce TP, on va faire la regression. C'est pour analyser la relation d'une variable par rapport à une ou plusieurs autres." ] }, { ... ... @@ -19,32 +19,56 @@ "cell_type": "markdown", "metadata": {}, "source": [ "On va utiliser les données Boston.\n", "https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\n", "\n", "Prix des maisons à Boston (cf le site pour les variables)\n", "https://scikit-learn.org/stable/datasets/index.html#boston-dataset\n", "\n", "Importez les libraries de ce matin: `numpy` et `scikit datasets`.\n", "Consultation de la doc du dataset\n", "\n", "Chargement du dataset boston" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse exploratoire et préparation du dataset\n", "Étudier les corrélations" "Étudier les corrélations en utilisant `np.corrcoef`" ] }, { "cell_type": "markdown", "cell_type": "code", "execution_count": null, "metadata": {}, "source": [ "Split du dataset boston" ] "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##" "Split du dataset boston\n", "\n", "Pour cela, utilisez la fonction scikit-learn `sklearn.model_selection.train_test_split`. Importez cette méthode, " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -54,6 +78,13 @@ "Trouver le modèle sur scikit learn." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -61,15 +92,28 @@ "Run sur boston. afficher les coef de chaque features. Quelles features sont significative?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arbre de régression\n", "Rappel modèle\n", "image" "![](https://fr.wikipedia.org/wiki/Arbre_de_d%C3%A9cision#/media/File:Arbre_de_decision.jpg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -77,6 +121,13 @@ "Essayer avec une profondeur max de 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -84,6 +135,13 @@ "Essayer avec une profondeur max de 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -91,6 +149,13 @@ "Essayer avec une profondeur max de 10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -98,6 +163,13 @@ "Comparer les résultats" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -108,6 +180,13 @@ "modèle" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -115,6 +194,13 @@ "Essayer avec 3 arbres" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -122,6 +208,13 @@ "Essayer avec 10 arbres" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -129,6 +222,13 @@ "100 arbres" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -136,6 +236,13 @@ "Comparer avec les arbres de régression. Quels sont les avantages?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -143,6 +250,13 @@ "_optionel_ Tracer le résultat avec 1 arbre, 3 arbres et 100 arbres " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, ... ... @@ -152,6 +266,13 @@ "\n", "Faire une régression sur le résultat d'une PCA (touchy)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { ... ... @@ -170,7 +291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" "version": "3.7.2" } }, "nbformat": 4, ... ...
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