Commit 36bbb85b authored by Long Le's avatar Long Le

TP régression (pas fini)

parent 499801d2
......@@ -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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