{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "2021_1_(완성본)_분류(인공지능과_미래사회).ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyPa37NuIC69rCbAeNXSoso1", "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": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "w_txkKjLe3kg" }, "source": [ "\n", "
\n", "* 레이블 = 정답" ] }, { "cell_type": "markdown", "metadata": { "id": "7nAyihHAfoF_" }, "source": [ "* 꽃 사진을 찍어서 품종을 알려주는 다음 꽃검색 \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "96ct-H4XhMyp" }, "source": [ "### 우리는 붓꽃의 품종을 구분해보자.\n", "\n", "\n", "머신러닝 데이터가 많은 사이트 kaggle에서 붓꽃 데이터를 다운받아 실행해보기 \n", "\n", "\n", "\n", "https://www.kaggle.com/uciml/iris" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3FyaEAjTEu-Y", "outputId": "e16d747f-d3a9-4fc1-c261-cc29a27b08dd" }, "source": [ "! git clone https://github.com/Ahnjihye/2021-AI-class.git" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Cloning into '2021-AI-class'...\n", "remote: Enumerating objects: 131, done.\u001b[K\n", "remote: Counting objects: 100% (131/131), done.\u001b[K\n", "remote: Compressing objects: 100% (128/128), done.\u001b[K\n", "remote: Total 131 (delta 43), reused 0 (delta 0), pack-reused 0\u001b[K\n", "Receiving objects: 100% (131/131), 3.86 MiB | 14.67 MiB/s, done.\n", "Resolving deltas: 100% (43/43), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "TUBlPF8FMgIE" }, "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"2021-AI-class/kor_iris.csv\", encoding = 'cp949') #데이터 불러오기" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 408 }, "id": "IjiTTrslO0ZU", "outputId": "3ebd022d-45e1-42f9-9d2f-5329bc8f273b" }, "source": [ "data" ], "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
번호꽃받침길이꽃받침너비꽃잎길이꽃잎너비품종
015.13.51.40.2세토사
124.93.01.40.2세토사
234.73.21.30.2세토사
344.63.11.50.2세토사
455.03.61.40.2세토사
.....................
1451466.73.05.22.3버지니카
1461476.32.55.01.9버지니카
1471486.53.05.22.0버지니카
1481496.23.45.42.3버지니카
1491505.93.05.11.8버지니카
\n", "

150 rows × 6 columns

\n", "
" ], "text/plain": [ " 번호 꽃받침길이 꽃받침너비 꽃잎길이 꽃잎너비 품종\n", "0 1 5.1 3.5 1.4 0.2 세토사\n", "1 2 4.9 3.0 1.4 0.2 세토사\n", "2 3 4.7 3.2 1.3 0.2 세토사\n", "3 4 4.6 3.1 1.5 0.2 세토사\n", "4 5 5.0 3.6 1.4 0.2 세토사\n", ".. ... ... ... ... ... ...\n", "145 146 6.7 3.0 5.2 2.3 버지니카\n", "146 147 6.3 2.5 5.0 1.9 버지니카\n", "147 148 6.5 3.0 5.2 2.0 버지니카\n", "148 149 6.2 3.4 5.4 2.3 버지니카\n", "149 150 5.9 3.0 5.1 1.8 버지니카\n", "\n", "[150 rows x 6 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 3 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 268 }, "id": "2w59F6WvO1fC", "outputId": "0c02c2d6-9716-4a3e-b310-b50bed493e1b" }, "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(data['꽃받침길이'], data['꽃받침너비'])\n", "plt.show()" ], "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "vEBeQIL4Pk5Z", "outputId": "2ce6411e-cb4e-4ed9-d949-10459e49e667" }, "source": [ "# 품종이 세토사인것만 골라내기\n", "data[data['품종']=='세토사']" ], "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
번호꽃받침길이꽃받침너비꽃잎길이꽃잎너비품종
015.13.51.40.2세토사
124.93.01.40.2세토사
234.73.21.30.2세토사
344.63.11.50.2세토사
455.03.61.40.2세토사
565.43.91.70.4세토사
674.63.41.40.3세토사
785.03.41.50.2세토사
894.42.91.40.2세토사
9104.93.11.50.1세토사
10115.43.71.50.2세토사
11124.83.41.60.2세토사
12134.83.01.40.1세토사
13144.33.01.10.1세토사
14155.84.01.20.2세토사
15165.74.41.50.4세토사
16175.43.91.30.4세토사
17185.13.51.40.3세토사
18195.73.81.70.3세토사
19205.13.81.50.3세토사
20215.43.41.70.2세토사
21225.13.71.50.4세토사
22234.63.61.00.2세토사
23245.13.31.70.5세토사
24254.83.41.90.2세토사
25265.03.01.60.2세토사
26275.03.41.60.4세토사
27285.23.51.50.2세토사
28295.23.41.40.2세토사
29304.73.21.60.2세토사
30314.83.11.60.2세토사
31325.43.41.50.4세토사
32335.24.11.50.1세토사
33345.54.21.40.2세토사
34354.93.11.50.1세토사
35365.03.21.20.2세토사
36375.53.51.30.2세토사
37384.93.11.50.1세토사
38394.43.01.30.2세토사
39405.13.41.50.2세토사
40415.03.51.30.3세토사
41424.52.31.30.3세토사
42434.43.21.30.2세토사
43445.03.51.60.6세토사
44455.13.81.90.4세토사
45464.83.01.40.3세토사
46475.13.81.60.2세토사
47484.63.21.40.2세토사
48495.33.71.50.2세토사
49505.03.31.40.2세토사
\n", "
" ], "text/plain": [ " 번호 꽃받침길이 꽃받침너비 꽃잎길이 꽃잎너비 품종\n", "0 1 5.1 3.5 1.4 0.2 세토사\n", "1 2 4.9 3.0 1.4 0.2 세토사\n", "2 3 4.7 3.2 1.3 0.2 세토사\n", "3 4 4.6 3.1 1.5 0.2 세토사\n", "4 5 5.0 3.6 1.4 0.2 세토사\n", "5 6 5.4 3.9 1.7 0.4 세토사\n", "6 7 4.6 3.4 1.4 0.3 세토사\n", "7 8 5.0 3.4 1.5 0.2 세토사\n", "8 9 4.4 2.9 1.4 0.2 세토사\n", "9 10 4.9 3.1 1.5 0.1 세토사\n", "10 11 5.4 3.7 1.5 0.2 세토사\n", "11 12 4.8 3.4 1.6 0.2 세토사\n", "12 13 4.8 3.0 1.4 0.1 세토사\n", "13 14 4.3 3.0 1.1 0.1 세토사\n", "14 15 5.8 4.0 1.2 0.2 세토사\n", "15 16 5.7 4.4 1.5 0.4 세토사\n", "16 17 5.4 3.9 1.3 0.4 세토사\n", "17 18 5.1 3.5 1.4 0.3 세토사\n", "18 19 5.7 3.8 1.7 0.3 세토사\n", "19 20 5.1 3.8 1.5 0.3 세토사\n", "20 21 5.4 3.4 1.7 0.2 세토사\n", "21 22 5.1 3.7 1.5 0.4 세토사\n", "22 23 4.6 3.6 1.0 0.2 세토사\n", "23 24 5.1 3.3 1.7 0.5 세토사\n", "24 25 4.8 3.4 1.9 0.2 세토사\n", "25 26 5.0 3.0 1.6 0.2 세토사\n", "26 27 5.0 3.4 1.6 0.4 세토사\n", "27 28 5.2 3.5 1.5 0.2 세토사\n", "28 29 5.2 3.4 1.4 0.2 세토사\n", "29 30 4.7 3.2 1.6 0.2 세토사\n", "30 31 4.8 3.1 1.6 0.2 세토사\n", "31 32 5.4 3.4 1.5 0.4 세토사\n", "32 33 5.2 4.1 1.5 0.1 세토사\n", "33 34 5.5 4.2 1.4 0.2 세토사\n", "34 35 4.9 3.1 1.5 0.1 세토사\n", "35 36 5.0 3.2 1.2 0.2 세토사\n", "36 37 5.5 3.5 1.3 0.2 세토사\n", "37 38 4.9 3.1 1.5 0.1 세토사\n", "38 39 4.4 3.0 1.3 0.2 세토사\n", "39 40 5.1 3.4 1.5 0.2 세토사\n", "40 41 5.0 3.5 1.3 0.3 세토사\n", "41 42 4.5 2.3 1.3 0.3 세토사\n", "42 43 4.4 3.2 1.3 0.2 세토사\n", "43 44 5.0 3.5 1.6 0.6 세토사\n", "44 45 5.1 3.8 1.9 0.4 세토사\n", "45 46 4.8 3.0 1.4 0.3 세토사\n", "46 47 5.1 3.8 1.6 0.2 세토사\n", "47 48 4.6 3.2 1.4 0.2 세토사\n", "48 49 5.3 3.7 1.5 0.2 세토사\n", "49 50 5.0 3.3 1.4 0.2 세토사" ] }, "metadata": { "tags": [] }, "execution_count": 5 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6te_adbnQB-j", "outputId": "cf76c09d-0002-41a0-fff1-5cbb7cc31a48" }, "source": [ "# 세토사인 붓꽃의 꽃받침길이\n", "data[data['품종']=='세토사']['꽃받침길이']" ], "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 5.1\n", "1 4.9\n", "2 4.7\n", "3 4.6\n", "4 5.0\n", "5 5.4\n", "6 4.6\n", "7 5.0\n", "8 4.4\n", "9 4.9\n", "10 5.4\n", "11 4.8\n", "12 4.8\n", "13 4.3\n", "14 5.8\n", "15 5.7\n", "16 5.4\n", "17 5.1\n", "18 5.7\n", "19 5.1\n", "20 5.4\n", "21 5.1\n", "22 4.6\n", "23 5.1\n", "24 4.8\n", "25 5.0\n", "26 5.0\n", "27 5.2\n", "28 5.2\n", "29 4.7\n", "30 4.8\n", "31 5.4\n", "32 5.2\n", "33 5.5\n", "34 4.9\n", "35 5.0\n", "36 5.5\n", "37 4.9\n", "38 4.4\n", "39 5.1\n", "40 5.0\n", "41 4.5\n", "42 4.4\n", "43 5.0\n", "44 5.1\n", "45 4.8\n", "46 5.1\n", "47 4.6\n", "48 5.3\n", "49 5.0\n", "Name: 꽃받침길이, dtype: float64" ] }, "metadata": { "tags": [] }, "execution_count": 6 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 269 }, "id": "qCRtycqGPDdz", "outputId": "2c4a9537-d014-4f21-a374-44291a7e4c79" }, "source": [ "# 세토사 산점도 그리기\n", "# x축은 세토사의 꽃받침길이, y축은 세토사의 꽃받침너비\n", "plt.scatter(data[data['품종']=='세토사']['꽃받침길이'], data[data['품종']=='세토사']['꽃받침너비'], color='indigo')\n", "plt.show()" ], "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 268 }, "id": "d2C3XdthPQoi", "outputId": "2114ebd1-76c5-417d-a23e-63281bd24033" }, "source": [ "plt.scatter(data[data['품종']=='세토사']['꽃받침길이'], data[data['품종']=='세토사']['꽃받침너비'], color='indigo')\n", "plt.scatter(data[data['품종']=='버시칼라']['꽃받침길이'], data[data['품종']=='버시칼라']['꽃받침너비'], color='crimson')\n", "plt.scatter(data[data['품종']=='버지니카']['꽃받침길이'], data[data['품종']=='버지니카']['꽃받침너비'], color='limegreen')\n", "\n", "plt.show()" ], "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "Qrkj31FBRv80" }, "source": [ "#### 5) 이번에는 꽃잎 길이가 x축, 꽃잎 너비가 y축인 산점도를 붓꽃 품종별로 그려보자. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "YwNMNQ3tPUgi", "outputId": "3bb2c09c-f8fe-4774-929b-41776888db8e" }, "source": [ "plt.scatter(data[data['품종']=='세토사']['꽃잎길이'], data[data['품종']=='세토사']['꽃잎너비'], color='indigo')\n", "plt.scatter(data[data['품종']=='버시칼라']['꽃잎길이'], data[data['품종']=='버시칼라']['꽃잎너비'], color='crimson')\n", "plt.scatter(data[data['품종']=='버지니카']['꽃잎길이'], data[data['품종']=='버지니카']['꽃잎너비'], color='limegreen')\n", "\n", "plt.show()" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "Rj18jcj_R9kz" }, "source": [ "#### Q. 위 그래프는 붓꽃 품종 세 개를 잘 구분하나요? " ] }, { "cell_type": "markdown", "metadata": { "id": "l8FbTguwIejM" }, "source": [ "# 4. 붓꽃의 품종을 구분하는 결정트리 만들어보기 \n", "\n", "#### 결정 트리 (Decision Tree)\n", "\n", "

\n", "\n", "\n", "

\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OQY5qiXMxGQT" }, "source": [ "#### 머신러닝 라이브러리 sklearn \n", "\n", "\n", "\n", "* 머신러닝에는 다양한 알고리즘이 존재\n", "* 대부분의 머신러닝 알고리즘이 sklearn에 구현되어 있음\n", "* sklearn의 의사결정트리를 사용하여 인공지능 학습을 시키고, 예측을 수행해보자\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fL3cTUzvS13U" }, "source": [ "\n", "\n", "```\n", "# 의사결정트리 기능 포함시키기\n", "from sklearn.tree import DecisionTreeClassifier\n", "```\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 408 }, "id": "tgZSiLJFUdDV", "outputId": "bdb5dc95-dc96-4da8-d904-4d5820222d4d" }, "source": [ "# data에서 꽃받침정보는 제외하고, 꽃잎 정보만 선택하여 학습시키기\n", "data.loc[ : , '꽃잎길이':'꽃잎너비']" ], "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
꽃잎길이꽃잎너비
01.40.2
11.40.2
21.30.2
31.50.2
41.40.2
.........
1455.22.3
1465.01.9
1475.22.0
1485.42.3
1495.11.8
\n", "

150 rows × 2 columns

\n", "
" ], "text/plain": [ " 꽃잎길이 꽃잎너비\n", "0 1.4 0.2\n", "1 1.4 0.2\n", "2 1.3 0.2\n", "3 1.5 0.2\n", "4 1.4 0.2\n", ".. ... ...\n", "145 5.2 2.3\n", "146 5.0 1.9\n", "147 5.2 2.0\n", "148 5.4 2.3\n", "149 5.1 1.8\n", "\n", "[150 rows x 2 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 11 } ] }, { "cell_type": "code", "metadata": { "id": "jtsHHCGfR7NA" }, "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "\n", "model = DecisionTreeClassifier(max_depth=2)" ], "execution_count": 12, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "G0aP9fLaTA2S", "outputId": "a4bb33dc-b4a0-42d2-9dbb-b706c991be2f" }, "source": [ "# 학습시키기 : 꽃잎길이, 꽃잎너비로 품종을 구분하게끔 학습시키기\n", "model.fit(data.loc[ : , '꽃잎길이':'꽃잎너비'], data['품종'])" ], "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n", " max_depth=2, max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort='deprecated',\n", " random_state=None, splitter='best')" ] }, "metadata": { "tags": [] }, "execution_count": 13 } ] }, { "cell_type": "markdown", "metadata": { "id": "tg6wI5pOYl3r" }, "source": [ "인공지능이 만든 결정 트리 확인하기 \n", "\n", "```\n", "import graphviz\n", "from sklearn.tree import export_graphviz\n", "\n", "# 결정 트리를 시각화하여 tree.dot 파일로 내보내는 코드\n", "export_graphviz(model, out_file ='tree.dot',\n", " feature_names = [학습데이터 속성명], \n", " class_names = [학습결과로 판단할 분류],\n", " filled = True, rounded = True, impurity = False)\n", "\n", "# 위에서 만든 tree.dot을 화면에 보이게 하기\n", "with open(\"tree.dot\") as f:\n", " dot_graph = f.read()\n", "display(graphviz.Source(dot_graph))\n", "```" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 378 }, "id": "xfq6_qU6TODs", "outputId": "27131547-acf6-48bd-afe9-2e3b09a841f4" }, "source": [ "import graphviz\n", "from sklearn.tree import export_graphviz\n", " \n", "# 결정 트리를 시각화하여 tree.dot 파일로 내보내는 코드\n", "export_graphviz(model, out_file ='tree.dot',\n", " feature_names = ['꽃잎 길이', '꽃잎 너비'], \n", " class_names = ['세토사','버시칼라','버지니카'],\n", " filled = True, rounded = True, impurity = False)\n", " \n", "# 위에서 만든 tree.dot을 화면에 보이게 하기\n", "with open(\"tree.dot\") as f:\n", " dot_graph = f.read()\n", "display(graphviz.Source(dot_graph))" ], "execution_count": 14, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "image/svg+xml": "\n\n\n\n\n\nTree\n\n\n\n0\n\n꽃잎 길이 <= 2.45\nsamples = 150\nvalue = [50, 50, 50]\nclass = 세토사\n\n\n\n1\n\nsamples = 50\nvalue = [0, 0, 50]\nclass = 버지니카\n\n\n\n0->1\n\n\nTrue\n\n\n\n2\n\n꽃잎 너비 <= 1.75\nsamples = 100\nvalue = [50, 50, 0]\nclass = 세토사\n\n\n\n0->2\n\n\nFalse\n\n\n\n3\n\nsamples = 54\nvalue = [49, 5, 0]\nclass = 세토사\n\n\n\n2->3\n\n\n\n\n\n4\n\nsamples = 46\nvalue = [1, 45, 0]\nclass = 버시칼라\n\n\n\n2->4\n\n\n\n\n\n" }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "K5Gwsb1HZBHr" }, "source": [ "### 위에서 작성했던 꽃잎길이-꽃잎너비 그래프에 분류하는 선 그려보기\n", "\n", "\n", "\n", "1. 첫번째 기준 꽃잎길이 <= 2.45\n", "\n", "\n", "2. 두번째 기준 꽃잎 너비 <= 1.75\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "1sQorZnAZE7N" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }