Machine Learning/Decision-Tree-Overfit.ipynb

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    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# Decision Trees ALWAYS Overfit. Here's A Lesser-Known Technique To Prevent It.\n",
    "\n",
    "Post Link: [Substack](https://www.blog.dailydoseofds.com/p/decision-trees-always-overfit-heres)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/feed/update/urn:li:share:7082296901307367424/)\n",
    "\n",
    "Twitter Post: [Twitter](https://twitter.com/_avichawla/status/1676531225542967298)"
   ]
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   "source": [
    "# Import needed functions from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
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   "execution_count": 2,
   "id": "de114070",
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     "text": [
      "1.0 0.943014705882353\n"
     ]
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   "source": [
    "# Generate some synthetic data to train the decision tree on\n",
    "X, y = make_classification(\n",
    "    n_samples=1000, \n",
    "    n_features=2, \n",
    "    n_informative=2, \n",
    "    n_redundant=0,\n",
    "    n_repeated=0\n",
    ")\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size=0.33, random_state=0)\n",
    "\n",
    "# Build a decision tree using the default configuration\n",
    "tree_a = DecisionTreeClassifier(random_state=0)\n",
    "tree_a.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the performance of the decision tree on the training data\n",
    "train_score = tree_a.score(X_train, y_train)\n",
    "roc_auc = roc_auc_score(y_test, tree_a.predict_proba(X_test)[:,1])\n",
    "print(train_score, roc_auc)"
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))\n",
    "Z = tree_a.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.contourf(xx, yy, Z, alpha=0.5, cmap='coolwarm_r')\n",
    "ax.set_title(\"Decision Region Plot\", fontsize = 20, fontweight = \"bold\")\n",
    "ax.set_xticklabels([])\n",
    "ax.set_yticklabels([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c20cbae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9731343283582089 0.9778308823529411\n"
     ]
    }
   ],
   "source": [
    "# Build a second decision tree, this time using a non-zero value for ccp_alpha\n",
    "tree_b = DecisionTreeClassifier(ccp_alpha=0.005, random_state=0)\n",
    "tree_b.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the performance of the second decision tree on the training data\n",
    "train_score = tree_b.score(X_train, y_train)\n",
    "roc_auc = roc_auc_score(y_test, tree_b.predict_proba(X_test)[:,1])\n",
    "print(train_score, roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c3eeab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport numpy as np'); }\n",
       "    "
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))\n",
    "Z = tree_b.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.contourf(xx, yy, Z, alpha=0.5, cmap='coolwarm_r')\n",
    "ax.set_title(\"Decision Region Plot\", fontsize = 20, fontweight = \"bold\")\n",
    "ax.set_xticklabels([])\n",
    "ax.set_yticklabels([])\n",
    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# \n",
    "\n",
    "Post Link: [Substack]()\n",
    "\n",
    "LinkedIn Post: [LinkedIn]()\n",
    "\n",
    "Twitter Post: [Twitter]()\n",
    "plt.show()\n"
   ]
  }
 ],
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