ML - Naive Bayes.ipynb

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     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "# load the iris datasets\n",
    "dataset = datasets.load_iris()"
   ]
  },
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   "execution_count": 14,
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     "execution_count": 14,
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   ],
   "source": [
    "dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit a Naive Bayes model to the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.33, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB(priors=None)\n"
     ]
    }
   ],
   "source": [
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions\n",
    "actual = y_test\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "[[15  0  0]\n",
      " [ 0 16  3]\n",
      " [ 0  0 16]]\n"
     ]
    }
   ],
   "source": [
    "# summarize the fit of the model\n",
    "print(metrics.confusion_matrix(actual, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        15\n",
      "          1       1.00      0.84      0.91        19\n",
      "          2       0.84      1.00      0.91        16\n",
      "\n",
      "avg / total       0.95      0.94      0.94        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(actual, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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     "execution_count": 29,
     "metadata": {},
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   ],
   "source": [
    "metrics.explained_variance_score(actual, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# metrics.calinski_harabaz_score(expected[:, np.newarray], predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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