Commit 1d6729ce by Sylvain Marchienne

### Improvements clustering

parent 7e6d2252
 ... ... @@ -42,7 +42,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ "Générer 4 datasets, avec les paramètres suivants: n = 100, noise = .05, random_state = 8, cluster_std=[1.0, 2.5, 0.5]." "Générer 4 datasets, avec les paramètres suivants: `n = 100`, `noise = .05`, `random_state = 8`, `cluster_std=[1.0, 2.5, 0.5]`." ] }, { ... ... @@ -56,36 +56,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ "Tracer les données générées dans le plan (\$R^2\$).\n", "Commenter la difficulté du clustering." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Formatage du jeu de données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour entrainer nos algorithmes, on va splitter notre jeu de données en 3 sous-jeux de données: \n", "- train\n", "- validation\n", "- test\n", "Tracer les données générées dans le plan (\$R^2\$). Utiliser:\n", "```\n", "fig, ax = plt.subplots()\n", "plt.scatter(X[:, 0], X[:, 1], s=10)\n", "plt.show()\n", "```\n", "\n", "Pourquoi est-ce nécessaire?\n", "\n", "Pour cela, utilisez la fonction scikit-learn `sklearn.model_selection.train_test_split`. Importez cette méthode, " "Commenter la difficulté du clustering." ] }, { ... ... @@ -257,7 +235,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" "version": "3.7.1" }, "toc": { "colors": { ... ...
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