python/comparing-classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Classifiers for Building Classification Models\n",
    "\n",
    "Chanin Nantasenamat\n",
    "\n",
    "<i>Data Professor YouTube channel, http://youtube.com/dataprofessor </i>\n",
    "\n",
    "In this Jupyter notebook, we will be comparing the performance of different classifiers for building classification models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Make synthetic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Generate the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = make_classification(n_samples=1000, n_classes=2, n_features=5, n_redundant=0, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Let's examine the data dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data split (80/20 ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Let's examine the data dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((800, 5), (800,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200, 5), (200,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Build Classification Models\n",
    "\n",
    "Modified from: https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Defining learning classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Nearest_Neighbors\", \"Linear_SVM\", \"Polynomial_SVM\", \"RBF_SVM\", \"Gaussian_Process\",\n",
    "         \"Gradient_Boosting\", \"Decision_Tree\", \"Extra_Trees\", \"Random_Forest\", \"Neural_Net\", \"AdaBoost\",\n",
    "         \"Naive_Bayes\", \"QDA\", \"SGD\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(kernel=\"poly\", degree=3, C=0.025),\n",
    "    SVC(kernel=\"rbf\", C=1, gamma=2),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    GradientBoostingClassifier(n_estimators=100, learning_rate=1.0),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    ExtraTreesClassifier(n_estimators=10, min_samples_split=2),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=100),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(n_estimators=100),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "    SGDClassifier(loss=\"hinge\", penalty=\"l2\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Build Model, Apply Model on Test Data & Record Accuracy Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "for name, clf in zip(names, classifiers):\n",
    "    clf.fit(X_train, Y_train)\n",
    "    score = clf.score(X_test, Y_test)\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.79,\n",
       " 0.83,\n",
       " 0.785,\n",
       " 0.78,\n",
       " 0.845,\n",
       " 0.82,\n",
       " 0.83,\n",
       " 0.76,\n",
       " 0.83,\n",
       " 0.84,\n",
       " 0.86,\n",
       " 0.825,\n",
       " 0.82,\n",
       " 0.77]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Analysis of Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Create data frame of *model performance*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nearest_Neighbors</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear_SVM</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polynomial_SVM</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RBF_SVM</td>\n",
       "      <td>0.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaussian_Process</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient_Boosting</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Decision_Tree</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Extra_Trees</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Random_Forest</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Neural_Net</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Naive_Bayes</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>QDA</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SGD</td>\n",
       "      <td>0.770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  score\n",
       "0   Nearest_Neighbors  0.790\n",
       "1          Linear_SVM  0.830\n",
       "2      Polynomial_SVM  0.785\n",
       "3             RBF_SVM  0.780\n",
       "4    Gaussian_Process  0.845\n",
       "5   Gradient_Boosting  0.820\n",
       "6       Decision_Tree  0.830\n",
       "7         Extra_Trees  0.760\n",
       "8       Random_Forest  0.830\n",
       "9          Neural_Net  0.840\n",
       "10           AdaBoost  0.860\n",
       "11        Naive_Bayes  0.825\n",
       "12                QDA  0.820\n",
       "13                SGD  0.770"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['name'] = names\n",
    "df['score'] = scores\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Adding colors to the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "            color:  #000000;\n",
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       "            color:  #000000;\n",
       "        }</style><table id=\"T_875c8232_66ce_11ea_9c0a_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >name</th>        <th class=\"col_heading level0 col1\" >score</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row0_col0\" class=\"data row0 col0\" >Nearest_Neighbors</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row0_col1\" class=\"data row0 col1\" >0.790000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row1_col0\" class=\"data row1 col0\" >Linear_SVM</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row1_col1\" class=\"data row1 col1\" >0.830000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row2_col0\" class=\"data row2 col0\" >Polynomial_SVM</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row2_col1\" class=\"data row2 col1\" >0.785000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row3_col0\" class=\"data row3 col0\" >RBF_SVM</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row3_col1\" class=\"data row3 col1\" >0.780000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row4_col0\" class=\"data row4 col0\" >Gaussian_Process</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row4_col1\" class=\"data row4 col1\" >0.845000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row5_col0\" class=\"data row5 col0\" >Gradient_Boosting</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row5_col1\" class=\"data row5 col1\" >0.820000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row6_col0\" class=\"data row6 col0\" >Decision_Tree</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row6_col1\" class=\"data row6 col1\" >0.830000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row7_col0\" class=\"data row7 col0\" >Extra_Trees</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row7_col1\" class=\"data row7 col1\" >0.760000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row8_col0\" class=\"data row8 col0\" >Random_Forest</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row8_col1\" class=\"data row8 col1\" >0.830000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row9_col0\" class=\"data row9 col0\" >Neural_Net</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row9_col1\" class=\"data row9 col1\" >0.840000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row10_col0\" class=\"data row10 col0\" >AdaBoost</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row10_col1\" class=\"data row10 col1\" >0.860000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row11_col0\" class=\"data row11 col0\" >Naive_Bayes</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row11_col1\" class=\"data row11 col1\" >0.825000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row12_col0\" class=\"data row12 col0\" >QDA</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row12_col1\" class=\"data row12 col1\" >0.820000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_875c8232_66ce_11ea_9c0a_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row13_col0\" class=\"data row13 col0\" >SGD</td>\n",
       "                        <td id=\"T_875c8232_66ce_11ea_9c0a_acde48001122row13_col1\" class=\"data row13 col1\" >0.770000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a25361410>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html\n",
    "\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "s = df.style.background_gradient(cmap=cm)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Bar plot of model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.barplot(y=\"name\", x=\"score\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}