libotti/overfiting.ipynb

## overfiting.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "overfiting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/libotti/b11a6dbe7033da7b13a1431c73930e82/overfiting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bldqGDk6eENH"
      },
      "source": [
        "#importando as bibliotecas\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cn3HOhoFeMLB"
      },
      "source": [
        "x=[1,2,4,5,6,8,9,10,11,12,14,15,20,25,30]\n",
        "y=[3,5,16,29,38,61,83,130,125,150,169,220,380,730,850]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QVyr1UIe65p"
      },
      "source": [
        " df=pd.DataFrame(list(zip(x, y)), \n",
        "               columns =['X', 'Y'])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnbMC8vofayq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "944c8d37-d482-44a1-8328-b1a01030c227"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>X</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    X    Y\n",
              "0   1    3\n",
              "1   2    5\n",
              "2   4   16\n",
              "3   5   29\n",
              "4   6   38\n",
              "5   8   61\n",
              "6   9   83\n",
              "7  10  130\n",
              "8  11  125\n",
              "9  12  150"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDuUzNm7fgnd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "ac23a383-0d05-40f2-e54f-685ecfb9d1de"
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "plt.scatter(df.X,df.Y,c=\"b\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8a7d7836d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9xkyMdXgJXH"
      },
      "source": [
        "linear = np.poly1d(np.polyfit(df['X'], df.Y, 1))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwDPwT89gvXA"
      },
      "source": [
        "quadratica = np.poly1d(np.polyfit(df.X, df.Y, 2))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0uzUsotgy3A"
      },
      "source": [
        "alto_grau = np.poly1d(np.polyfit(df.X, df.Y, 10))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQB8NGZVgkX2"
      },
      "source": [
        "t = np.linspace(0, 30, 200)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BYAht2khAV8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "d21ffeda-e879-4bb0-fffc-9ab50e7deec1"
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "plt.plot(df.X, df.Y, 'o', t, linear(t), '-')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8a7d734110>,\n",
              " <matplotlib.lines.Line2D at 0x7f8a7d25ac90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Mae1LR5haFY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "0f6a339b-2b68-4265-cada-e15c9aa8ee20"
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "plt.plot(df.X, df.Y, 'o', t, quadratica(t), '-')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8a7d1da250>,\n",
              " <matplotlib.lines.Line2D at 0x7f8a7d1da3d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_68TwJKhhxy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "8e031240-ebd6-4d37-b529-1230cd925bf1"
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "plt.plot(df.X, df.Y, 'o', t, alto_grau(t), '-')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8a7d14b1d0>,\n",
              " <matplotlib.lines.Line2D at 0x7f8a7d14b350>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRKrfKbf1ESn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cedb385-5c35-4743-8da0-f8812e7a77ad"
      },
      "source": [
        "from sklearn.metrics import r2_score #método para o cálculo do R2 (coeficiente de determinação)\n",
        "R_2 = r2_score(df.Y, alto_grau(df.X))  #realiza o cálculo do R2\n",
        "\n",
        "print(\"Coeficiente de Determinação (R2):\", R_2)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coeficiente de Determinação (R2): 0.9996058403686907\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxSamIF11mYS"
      },
      "source": [
        "previsao_segundo_grau=quadratica(27)\n",
        "previsao_alto_grau=alto_grau(27)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Amst6e7813kl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63a48323-190e-49d0-fb7f-9fdb0064a700"
      },
      "source": [
        "print(\"Previsão quadrática: {}\".format(previsao_segundo_grau))\n",
        "print(\"Previsão décimo grau: {}\".format(previsao_alto_grau))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Previsão quadrática: 731.5057775557983\n",
            "Previsão décimo grau: 8657.047680681988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_n_G1bDLBzpF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6421ee3-2f35-4ee9-f72e-70486073597c"
      },
      "source": [
        "27*27"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "729"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmwkcchXMPjp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "507fc8fb-7b86-4f1b-d397-8ba342c9e2d7"
      },
      "source": [
        "previsao_segundo_grau[0]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-04f2b01d3d01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprevisao_segundo_grau\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable."
          ]
        }
      ]
    }
  ]
}