Decision_Tree_Moons.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP9MO0pp0g18+QsjTrZkZhp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RylieWeaver9/Machine-Learning/blob/main/Decision_Tree_Moons.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "uAzr7on83b8e"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize arrays\n",
        "X = []\n",
        "Y = []\n",
        "\n",
        "# Run loop of accuracy vs size of dataset\n",
        "for i in range(1,6):\n",
        "  n_samples = 10**i\n",
        "\n",
        "  # Make dataset\n",
        "  X_moons, y_moons = make_moons(n_samples, noise=0.4, random_state=42)\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_moons, y_moons, test_size=0.2, random_state=42)\n",
        "  \n",
        "  # Find and set to best hyperparameters\n",
        "  params = {'max_leaf_nodes': list(range(2, 100)), 'max_depth': list(range(1, 7)), 'min_samples_split': [2, 3, 4]}\n",
        "  grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, cv=3)\n",
        "  grid_search_cv.fit(X_train, y_train)\n",
        "  grid_search_cv.best_estimator_\n",
        "  print(grid_search_cv.best_estimator_)\n",
        "\n",
        "  # Make prediction\n",
        "  y_pred = grid_search_cv.predict(X_test)\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "  print(accuracy)\n",
        "\n",
        "  # Append to array to plot\n",
        "  X.append(i)\n",
        "  Y.append(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjv2MhgpB0YU",
        "outputId": "ff98b666-5a1a-4ab1-cbc3-a72646a2dc3b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier(max_depth=1, max_leaf_nodes=2, random_state=42)\n",
            "1.0\n",
            "DecisionTreeClassifier(max_depth=3, max_leaf_nodes=4, random_state=42)\n",
            "0.9\n",
            "DecisionTreeClassifier(max_depth=2, max_leaf_nodes=4, random_state=42)\n",
            "0.855\n",
            "DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)\n",
            "0.8595\n",
            "DecisionTreeClassifier(max_depth=6, max_leaf_nodes=33, random_state=42)\n",
            "0.8574\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot\n",
        "plt.figure(figsize = (9,6))\n",
        "# plt.ylim(0,1)\n",
        "plt.scatter(X,Y, c='r')\n",
        "plt.plot(X,Y)\n",
        "\n",
        "# Label plot\n",
        "plt.title('Number of Samples vs Accuracy Plot')\n",
        "plt.xlabel('log_10(Number of Samples)')\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "# Show\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "AnMiAchHByr2",
        "outputId": "cfee0667-827d-4a67-8597-b94c6d807729"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The accuracy seems to decrease and plateau around 0.857 as the number of samples increases."
      ],
      "metadata": {
        "id": "tr9gyOSoHKov"
      }
    }
  ]
}