Commit 1d6729ce authored by Sylvain Marchienne's avatar 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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