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DataVenture
api-h19
Commits
1d6729ce
Commit
1d6729ce
authored
Jan 22, 2019
by
Sylvain Marchienne
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Improvements clustering
parent
7e6d2252
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TP/TP2_mardi/tp_clustering.ipynb
TP/TP2_mardi/tp_clustering.ipynb
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TP/TP2_mardi/tp_clustering.ipynb
View file @
1d6729ce
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@@ -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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