Lab 15 - Naive Bayes Demo.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5ab0e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_iris, load_breast_cancer\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "551d1094",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = load_iris()\n",
    "cancer_dataset = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da4d9e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# iris dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris_dataset.data, iris_dataset.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "694cefa9",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6be29014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a663203f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# breast cancer dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer_dataset.data, cancer_dataset.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31f6cb08",
   "metadata": {},
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    {
     "data": {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "190dfef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9370629370629371"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bb696c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76ec23a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e703a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4025db57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[48,  5],\n",
       "       [ 4, 86]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "314824c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce2adff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix for Breast Cancer Dataset')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(cm, annot=True)\n",
    "\n",
    "ax.set_xlabel(\"Predicted Diagnosis\", fontsize=14, labelpad=20)\n",
    "ax.xaxis.set_ticklabels(['Negative', 'Positive'])\n",
    "\n",
    "ax.set_ylabel('Actual Diagnosis', fontsize=14, labelpad=20)\n",
    "ax.yaxis.set_ticklabels(['Negative', 'Positive'])\n",
    "\n",
    "ax.set_title(\"Confusion Matrix for Breast Cancer Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f5a962",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}