naive_modelling.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
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   "source": [
    "from sklearn import datasets\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "(150, 4) (150,)\n"
     ]
    }
   ],
   "source": [
    "# Loading Iris dataset\n",
    "X, y = datasets.load_iris(return_X_y=True)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing a Random Forest with \n",
    "# arbitrary hyperparameters\n",
    "# max_depth kept as 2 since Iris has\n",
    "# only 4 features\n",
    "clf = RandomForestClassifier(n_estimators=10, max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean score : 0.95333\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n",
    "print(\"Mean score : {:.5f}\".format(scores.mean()))"
   ]
  }
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good