Model-Fitting-Train-Testing-Accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f12bfb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "x, y = make_classification(n_samples=50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04f59f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 20)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a973a833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62bacebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "features = pd.DataFrame(x)\n",
    "classes = pd.DataFrame(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fdc58c6",
   "metadata": {},
   "outputs": [
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       "             0         1         2         3         4         5         6   \\\n",
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       "\n",
       "             7         8         9         10        11        12        13  \\\n",
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       "\n",
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     "metadata": {},
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    }
   ],
   "source": [
    "features.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70c9a306",
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "classes.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "512acac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, train_size=0.65, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd0b8cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32500, 20)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c93ce79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17500, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f38eb39",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "depth = [ n for n in range(1, 21) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d64952a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f2d4a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth = 1, test_accuracy = 0.901, train_accuracy = 0.899\n",
      "depth = 2, test_accuracy = 0.901, train_accuracy = 0.899\n",
      "depth = 3, test_accuracy = 0.930, train_accuracy = 0.929\n",
      "depth = 4, test_accuracy = 0.933, train_accuracy = 0.934\n",
      "depth = 5, test_accuracy = 0.937, train_accuracy = 0.937\n",
      "depth = 6, test_accuracy = 0.941, train_accuracy = 0.945\n",
      "depth = 7, test_accuracy = 0.944, train_accuracy = 0.951\n",
      "depth = 8, test_accuracy = 0.943, train_accuracy = 0.955\n",
      "depth = 9, test_accuracy = 0.942, train_accuracy = 0.960\n",
      "depth = 10, test_accuracy = 0.940, train_accuracy = 0.964\n",
      "depth = 11, test_accuracy = 0.938, train_accuracy = 0.968\n",
      "depth = 12, test_accuracy = 0.936, train_accuracy = 0.972\n",
      "depth = 13, test_accuracy = 0.936, train_accuracy = 0.977\n",
      "depth = 14, test_accuracy = 0.935, train_accuracy = 0.980\n",
      "depth = 15, test_accuracy = 0.933, train_accuracy = 0.983\n",
      "depth = 16, test_accuracy = 0.932, train_accuracy = 0.986\n",
      "depth = 17, test_accuracy = 0.932, train_accuracy = 0.988\n",
      "depth = 18, test_accuracy = 0.928, train_accuracy = 0.990\n",
      "depth = 19, test_accuracy = 0.927, train_accuracy = 0.992\n",
      "depth = 20, test_accuracy = 0.927, train_accuracy = 0.993\n"
     ]
    }
   ],
   "source": [
    "for x in (depth):\n",
    "    dt = DecisionTreeClassifier(max_depth=x)\n",
    "    dt.fit(xtrain, ytrain)\n",
    "    \n",
    "    #train_accuracy\n",
    "    train_prediction = dt.predict(xtrain)\n",
    "    train_accuracy = dt.score(xtrain, ytrain)\n",
    "    train_scores.append(train_accuracy)\n",
    "    \n",
    "    #testing_accuracy\n",
    "    test_prediction = dt.predict(xtest)\n",
    "    test_accuracy = dt.score(xtest, ytest)\n",
    "    test_scores.append(test_accuracy)\n",
    "    \n",
    "    print(f'depth = {x}, test_accuracy = {test_accuracy:.3f}, train_accuracy = {train_accuracy:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b38f53d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "train_scores"
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    {
     "data": {
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    "test_scores"
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  {
   "cell_type": "code",
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   "id": "5b4e239a",
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    {
     "data": {
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       "[<matplotlib.lines.Line2D at 0x16538d3f4f0>]"
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     "execution_count": 19,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.plot(depth, train_scores, marker=\"o\")\n",
    "pyplot.plot(depth, test_scores, marker=\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4b2fa0e",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}