TP 4 RN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP 5 Réseaux de neurones - Multi classification et Propagation en avant\n",
    "Dans ce TP, nous aimerions faire une classification multiclasse. Pour ce faire, nous allons comparer entre une classification utilisant la regression logistique, et une classification en utilisant les réseaux de neurones.\n",
    "\n",
    "L'ensemble de données que nous allons utiliser est le même que celui utilisé durant le TP1, i.e. les images de chiffres manuscrits.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importation des librairies necessaires au travail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import random\n",
    "import cv2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture des fichiers de données\n",
    "Pour ce TP, nous allons lire les données à partir d'un fichier csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 401)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# données\n",
    "data = np.genfromtxt('data.csv', delimiter=',', dtype=float)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans ces données (data), les 400 premières colonnes representent les pixels de l'image (20x20), la dernière colonne represente la classe de l'image (chiffres de 0 à 9). (http://yann.lecun.com/exdb/mnist/)\n",
    "\n",
    "Chaque ligne represente un exemple de notre ensemble de données. \n",
    "\n",
    "Mettons ces données dans leus vecteurs correspondants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rajoutons l'ordonnée à l'origine theta 0\n",
    "intercept=np.ones((data.shape[0],1))\n",
    "X=np.column_stack((intercept,data[:,:-1])) # creation de la matrice de caracteristiques\n",
    "y=data[:,-1] # la colonne de la valeur cible 'la classe'\n",
    "# forcer y à avoir une seule colonne\n",
    "y = y.reshape( y.shape[0], 1)   # pour s'alligner correctement avec X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X (5000, 401)  y  (5000, 1)\n"
     ]
    }
   ],
   "source": [
    "print('X', X.shape ,' y ', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisation aléatoire de quelques données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x800 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "for i in range(13):\n",
    "    c = random.randint(X.shape[0])\n",
    "    a = X[c,1:].reshape((20, 20))  # extrait la ligne de X a partir de la 2eme colonne puisque 1st= intercept\n",
    "    a=np.transpose(a)\n",
    "    #plt.subplot(2,7,i+1)\n",
    "    plt.subplot(1,13,i+1)\n",
    "    plt.title('label '+ str(y[c]))\n",
    "    plt.imshow(a,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partie 1, régression logistique\n",
    "Dans cette partie, nous aimerions utiliser la régression logistique pour classifier nos images\n",
    "\n",
    "Rappelons que la régression logistique nous donne la probabilité d'appartenance (oui ou non) à la classe 1 (elle permet une classification binaire).\n",
    "\n",
    "Pour étendre la régression logistique à une multi-classification, nous allons utiliser une stratégie 1 contre tous.\n",
    "\n",
    "Nos paramètres $\\theta$ seront donc une matrice avec un nombre de lignes égale au nombre de classes, et avec un nombre de colones égale au nombre des caractéristiques + 1 (chaque ligne de la matrice $\\theta$ correspond aux paramètres d'un classifieur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Donc la matrice theta aura 10 lignes (nombre de classes)\n",
    "#et 400+1 colonnes ( 400 caracteristiques et 1 intercept) \n",
    "\n",
    "#On va utiliser La strategie 1 contre tous -> vous allez entraîner\n",
    "# 10 classifieurs binaires (un pour chaque classe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lrCostFunction (X, y, initial_theta, alpha, MaxIter,lambda_):\n",
    "    # dans cette fonction vous devez appliquer la regression logistique avec tout ce que ceci implique\n",
    "    # calcul du coût, minimisation du coût avec descente du gradient, et retour des paramètres theta pour une classe\n",
    "\n",
    "    m, n = X.shape # m : le nombre d'exemples , n: le nombre de caracteristiques\n",
    "    \n",
    "    theta = initial_theta.reshape((n, 1))\n",
    "    J_history = np.zeros((MaxIter, 1))  #Pour stocker les valeurs du cout\n",
    "    \n",
    "    for i in range(MaxIter):\n",
    "        # Calcul de l'hypothese 'h' en appliquant la sigmoid\n",
    "        z = np.dot(X, theta)\n",
    "        h = 1 / (1 + np.exp(-z))\n",
    "        \n",
    "\n",
    "        #calcul du cout en utilisant la formule du cout avec regularisation\n",
    "        J = (-1/m) * np.sum((y * np.log(h)) + ((1-y) * np.log(1-h))) + (lambda_/(2*m)) * np.sum(np.square(theta[1:]))\n",
    "        J_history[i] = J\n",
    "\n",
    "        #Calcul du gradient :\n",
    "        gradient = (1/m) * np.dot(X.T, (h - y))\n",
    "        gradient[1:] = gradient[1:] + (lambda_/m) * theta[1:]  # la regularisation (penalite) est ajoutee pour le par autre que l'intercept\n",
    "        \n",
    "        #mise a jour de theta\n",
    "        theta = theta - alpha * gradient\n",
    "    \n",
    "    return theta, J_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    g = 1 / (1 + np.exp(-z))\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictOnveVsAll (all_theta, X):\n",
    "    # ici en utilisant les paramètres calculés par la régression logisitique, \n",
    "    # nous aimerions retourner les etiquettes predites\n",
    "    \n",
    "    # Ici chaque classifieur retournera de probabilité, il faudra choisir \n",
    "    # la probabilité maximale \n",
    "    # de tous les classifieurs d'un exemple donné\n",
    "    # répeter pour tous les exmemples\n",
    "\n",
    "\n",
    "    # Calculer la probabilité de chaque classe pour chaque exemple\n",
    "    # probs est donc une matrice où chaque ligne correspond à un exemple et chaque colonne correspond à la probabilité prédite pour cette classe\n",
    "    probs = sigmoid(X.dot(all_theta.T))\n",
    "    # Choisir la classe ayant la probabilité la plus élevée pour chaque exemple\n",
    "    y_pred = np.argmax(probs, axis=1)\n",
    "    return y_pred.reshape((X.shape[0], 1))  # on redimensionne le tableau unidimentionnel en une matrice avec une suele colonne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 401)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes= np.unique(y)\n",
    "number_classes=classes.shape[0]\n",
    "all_theta = np.zeros((number_classes, X.shape[1]))\n",
    "all_theta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 1 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 2 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 3 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 4 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 5 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 6 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 7 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 8 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 9 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La forme de all_theta après le 10 ème classifieur est (10, 401)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MaxIter= 400\n",
    "lambda_= 0.1\n",
    "alpha = 0.6  # val de alpha essayees : 0.01 0.05 0.1 0.6 0.3 0.4 0.5\n",
    "# parametres initiaux pour chaque classifieur initialise a zero\n",
    "initial_theta=np.zeros((X.shape[1], 1))\n",
    "for i in range (number_classes):\n",
    "     # appel pour chaque classifieur\n",
    "    theta, J_history = lrCostFunction(X,(y==classes[i]).astype(int),initial_theta,alpha, MaxIter,lambda_)\n",
    "    #La partie (y==classes[i]).astype(int) cree un vecteur de même longueur que y, où chaque element est 1 si l'etiquette correspond a la classe actuelle classes[i], et 0 sinon.\n",
    "    all_theta[i,:] = theta.T\n",
    "    #affichage \n",
    "    print(\"La forme de all_theta après le {} ème classifieur est {}\".format(i+1, all_theta.shape))\n",
    "    # Affichage de l'historique J\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.plot(J_history, label='Historique J')\n",
    "    plt.xlabel('Itérations')\n",
    "    plt.ylabel('Coût')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = predictOnveVsAll(all_theta, X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qualité du classifieur RL\n",
    "\n",
    "Prédire des valeurs de y\n",
    "\n",
    "Ici il serait interessant de calculer la précision de notre classifieur\n",
    "\n",
    "Essayons de calculer ça avec \n",
    "\n",
    "moyenne(y==y-pred) * 100\n",
    "\n",
    "Ceci donnera un pourcentage de precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.28"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision = np.mean(y==y_pred)*100  #91.74 0.8  #0.6 91.28  #0.5 91.04 04 90.7\n",
    "precision \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Petit Rappel :\n",
    "Rappel :est une mesure de la capacité d'un modèle à identifier correctement les instances positives.\n",
    " Il se calcule comme le nombre d'instances positives correctement prédites divisé par le nombre total d'instances positives réelles dans l'ensemble de données. (VP/VP+FN)\n",
    "\n",
    "Precision: est une mesure de l'exactitude d'un modèle de classification. \n",
    " Elle se calcule comme le nombre d'instances correctement classées comme positives divisé par le nombre total d'instances classées comme positives, qu'elles soient correctes ou incorrectes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Précision: 0.9128\n",
      "Rappel: 0.974\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affichage des résultats\n",
    "precision = np.sum(y_pred.ravel() == y.ravel()) / len(y)\n",
    "recall = np.sum((y_pred.ravel() == 1) & (y.ravel() == 1)) / np.sum(y.ravel() == 1)\n",
    "print(\"Précision:\", precision)\n",
    "print(\"Rappel:\", recall)\n",
    "\n",
    "# Affichage de l'historique J\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(J_history, label='Historique J')\n",
    "plt.xlabel('Itérations')\n",
    "plt.ylabel('Coût')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Coût initial\n",
    "#initial_cost = J_history[0]\n",
    "#print(\"Coût initial:\", initial_cost)\n",
    "\n",
    "# Coût final\n",
    "#final_cost = J_history[-1]\n",
    "#print(\"Coût final:\", final_cost)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les valeurs hors de la diagonale principale représentent les erreurs de classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Calcul de la matrice de confusion\n",
    "conf_matrix = confusion_matrix(y.flatten(), y_pred.flatten())\n",
    "\n",
    "# Affichage de la matrice de confusion avec seaborn\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues', fmt='d')\n",
    "plt.title('Matrice de confusion')\n",
    "plt.xlabel('Classe prédite')\n",
    "plt.ylabel('Classe réelle')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partie 2: Réseaux de neurones\n",
    "Pour cette partie, nous choisissons une réseau simple:\n",
    "\n",
    "- une couche d'entrée avec 400 noeuds (20 x 20 pixels) + le biais\n",
    "- une couche cachée avec 25 noeuds\n",
    "- une couche de sortie avec 10 noeuds (nombre de classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 401)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# poids de la couche 1\n",
    "W1 = np.genfromtxt('W1.csv', delimiter=',', dtype=float)\n",
    "W1.shape\n",
    "\n",
    "\n",
    "#Chaque neurone de la coyche cachee a 401 poids associés \n",
    "#(400 pour les connexions avec les neurones de la couche d'entrée et 1 pour le biais)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 26)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# poids de la couche 2\n",
    "W2 = np.genfromtxt('W2.csv', delimiter=',', dtype=float)\n",
    "W2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer_size  = 400 \n",
    "hidden_layer_size = 25 \n",
    "num_labels = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prédiction**\n",
    "\n",
    "Appliquer une propagation en avant en utilisant les paramètres données pour prédir les classes de l'ensemble d'apprentissage.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict (W1, W2, X):\n",
    "    \n",
    "    # appliquer une propagation en avant\n",
    "    # !--- n'oubliez pas d'appliquer la sigmoid à chaque couche afin d'avoir les probabilités ---!\n",
    "    \n",
    "    # prédire la classe en choisissant la probabilité maximale parmi les 10 noeuds de sortie\n",
    "    # Nombre d'exemples dans X\n",
    "\n",
    "    m = y.shape[0]\n",
    "    # Calcul de la sortie de la couche cachée\n",
    "    z2 = np.dot(X, W1)\n",
    "    #Application de la fonction d'activation sigmoid à la sortie de la couche cachee\n",
    "    a2 = sigmoid(z2)\n",
    "    \n",
    "    a2 = np.hstack((np.ones((m, 1)), a2)) #ajout du biais\n",
    "\n",
    "    # Calcul de la sortie de la couche de sortie\n",
    "    z3 = np.dot(a2, W2)\n",
    "    a3 = sigmoid(z3)\n",
    "\n",
    "    # Prédiction de la classe en choisissant la probabilité maximale parmi les 10 noeuds de sortie\n",
    "    y_pred = np.argmax(a3, axis=1) + 1\n",
    "\n",
    "    return y_pred.reshape((m, 1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calcul de precision = nombre de valeurs bien prédites (ici sur toute la base X)\n",
    "y_pred = predict(W1.T, W2.T, X)\n",
    "precision = np.mean(y==y_pred)*100\n",
    "precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vérification de l'implementation\n",
    "Comparer vos algorithmes à ceux de scikitlearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision with sklearn is : 96.26\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(random_state=42,max_iter=1000)\n",
    "model.fit(X, y.squeeze())\n",
    "y_pred = model.predict(X)\n",
    "precision = np.mean(y==y_pred.reshape(y.shape[0],1))*100\n",
    "print(\"precision with sklearn is :\",precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'analyse montre que le modèle de régression logistique de scikit-learn est le plus performant avec une précision de 96,26%. Le classifieur RL est également solide avec une précision de 91.28%. Cependant, le réseau de neurones est moins performant avec une précision de 87,6%. Il est important de noter que la précision dépend des paramètres algorithmiques et du prétraitement des données."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Renforcement d'apprentissage\n",
    "Mettre ici toute idée qui pourrait renforcer votre apprentissage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Diagnostic des erreurs : Analyser les erreurs faites par le modèle pour comprendre les types de données sur lesquelles ils ont du mal à généraliser. Cela peut nous aider à identifier des pistes pour améliorer vos modèles ou à recueillir des données supplémentaires pour les zones difficiles.\n",
    "\n",
    "* Utilisation de techniques avancées : Explorer des techniques plus avancées telles que les réseaux de neurones profonds, les méthodes d'apprentissage par transfert, ou les architectures de réseau plus complexes pour voir si elles peuvent améliorer les performances de votre modèle.\n",
    "\n",
    "* Utilisation d'ensembles de modèles : Expérimenter avec des techniques d'ensemble telles que le bagging, le boosting ou le stacking pour combiner les prédictions de plusieurs modèles. Cela peut souvent améliorer les performances par rapport à l'utilisation d'un seul modèle."
   ]
  }
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