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serithemage/pycaret.ipynb

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PyCaret튜토리얼-회귀.ipynb
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pycaret.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/serithemage/75fcb7cf439ba503a3b1d8911d1404a9/pycaret.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# PyCaret 튜토리얼\n",
"\n",
"## PyCaret이란?\n",
"PyCaret은 본질적으로 scikit-learn , XGBoost , Microsoft LightGBM , spaCy 등과 같은 여러 기계 학습 라이브러리 및 프레임워크를 좀 더 간편하게 사용할 수 있게 해 주는 Python래퍼 입니다. \n",
"\n",
"PyCaret은 다음의 분들에게 적합합니다.\n",
"- 생산성을 높이고자 하는 숙련된 데이터 과학자\n",
"- 적은양의 코딩으로 가능한 기계학습 솔루션을 선호하는 시민 데이터 과학자(Citizen Data Scientists)\n",
"- 데이터 과학을 배우려는 학생\n",
"- 개념 증명 프로젝트 구축에 관련된 데이터 과학자 및 컨설턴트\n",
"\n",
"![](https://i2.wp.com/pycaret.org/wp-content/uploads/2020/07/pycaret2-features.png?resize=1033%2C613&ssl=1)"
],
"metadata": {
"id": "CJeNY6nyrhng"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mKn8hdKiyTjV"
},
"outputs": [],
"source": [
"!pip install pycaret -q"
]
},
{
"cell_type": "code",
"source": [
"from pycaret.regression import *\n",
"from pycaret.datasets import get_data"
],
"metadata": {
"id": "hHpJ82b1yWHu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"PyCaret는 기계학습 공부에 사용할 수 있는 다양한 [데이터셋](https://pycaret.org/get-data/)을 제공합니다.\n",
"\n",
"여기서는 다이아몬드의 가격을 예측하는 데이터셋을 사용해 보겠습니다. 다이아몬드는 무게, 색, 가공등에 따라 감정되고 이에 대해 가격이 책정됩니다. \n",
"다이아몬드 데이터셋의 상세는 [여기](https://www.kaggle.com/shivam2503/diamonds)에서 살펴볼 수 있습니다."
],
"metadata": {
"id": "BZQ82Q7n3h0c"
}
},
{
"cell_type": "code",
"source": [
"dataset = get_data('diamond')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "llIQ5DrFyyTD",
"outputId": "9e06671d-5af8-4d22-ca49-8bf465e958ec"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"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>Carat Weight</th>\n",
" <th>Cut</th>\n",
" <th>Color</th>\n",
" <th>Clarity</th>\n",
" <th>Polish</th>\n",
" <th>Symmetry</th>\n",
" <th>Report</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.10</td>\n",
" <td>Ideal</td>\n",
" <td>H</td>\n",
" <td>SI1</td>\n",
" <td>VG</td>\n",
" <td>EX</td>\n",
" <td>GIA</td>\n",
" <td>5169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.83</td>\n",
" <td>Ideal</td>\n",
" <td>H</td>\n",
" <td>VS1</td>\n",
" <td>ID</td>\n",
" <td>ID</td>\n",
" <td>AGSL</td>\n",
" <td>3470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.85</td>\n",
" <td>Ideal</td>\n",
" <td>H</td>\n",
" <td>SI1</td>\n",
" <td>EX</td>\n",
" <td>EX</td>\n",
" <td>GIA</td>\n",
" <td>3183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.91</td>\n",
" <td>Ideal</td>\n",
" <td>E</td>\n",
" <td>SI1</td>\n",
" <td>VG</td>\n",
" <td>VG</td>\n",
" <td>GIA</td>\n",
" <td>4370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.83</td>\n",
" <td>Ideal</td>\n",
" <td>G</td>\n",
" <td>SI1</td>\n",
" <td>EX</td>\n",
" <td>EX</td>\n",
" <td>GIA</td>\n",
" <td>3171</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Carat Weight Cut Color Clarity Polish Symmetry Report Price\n",
"0 1.10 Ideal H SI1 VG EX GIA 5169\n",
"1 0.83 Ideal H VS1 ID ID AGSL 3470\n",
"2 0.85 Ideal H SI1 EX EX GIA 3183\n",
"3 0.91 Ideal E SI1 VG VG GIA 4370\n",
"4 0.83 Ideal G SI1 EX EX GIA 3171"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"이제 학습을 위해 데이터를 설정합시다."
],
"metadata": {
"id": "Qa1DIUQvLWOJ"
}
},
{
"cell_type": "code",
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"exp = setup(dataset, target='Price')"
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" <tr>\n",
" <th>0</th>\n",
" <td>session_id</td>\n",
" <td>4417</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Target</td>\n",
" <td>Price</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Original Data</td>\n",
" <td>(6000, 8)</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Missing Values</td>\n",
" <td>False</td>\n",
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" <th>4</th>\n",
" <td>Numeric Features</td>\n",
" <td>1</td>\n",
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" <th>5</th>\n",
" <td>Categorical Features</td>\n",
" <td>6</td>\n",
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" <th>6</th>\n",
" <td>Ordinal Features</td>\n",
" <td>False</td>\n",
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" <th>7</th>\n",
" <td>High Cardinality Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>High Cardinality Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Transformed Train Set</td>\n",
" <td>(4199, 28)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Transformed Test Set</td>\n",
" <td>(1801, 28)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Shuffle Train-Test</td>\n",
" <td>True</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>Stratify Train-Test</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Fold Generator</td>\n",
" <td>KFold</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Fold Number</td>\n",
" <td>10</td>\n",
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" <th>15</th>\n",
" <td>CPU Jobs</td>\n",
" <td>-1</td>\n",
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" <th>16</th>\n",
" <td>Use GPU</td>\n",
" <td>False</td>\n",
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" <th>17</th>\n",
" <td>Log Experiment</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>18</th>\n",
" <td>Experiment Name</td>\n",
" <td>reg-default-name</td>\n",
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" <tr>\n",
" <th>19</th>\n",
" <td>USI</td>\n",
" <td>79cc</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>Imputation Type</td>\n",
" <td>simple</td>\n",
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" <tr>\n",
" <th>21</th>\n",
" <td>Iterative Imputation Iteration</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Numeric Imputer</td>\n",
" <td>mean</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Iterative Imputation Numeric Model</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Categorical Imputer</td>\n",
" <td>constant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Iterative Imputation Categorical Model</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Unknown Categoricals Handling</td>\n",
" <td>least_frequent</td>\n",
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" <tr>\n",
" <th>27</th>\n",
" <td>Normalize</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Normalize Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Transformation</td>\n",
" <td>False</td>\n",
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" <th>30</th>\n",
" <td>Transformation Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>PCA</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>PCA Method</td>\n",
" <td>None</td>\n",
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" <tr>\n",
" <th>33</th>\n",
" <td>PCA Components</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Ignore Low Variance</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Combine Rare Levels</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Rare Level Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Numeric Binning</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Remove Outliers</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Outliers Threshold</td>\n",
" <td>None</td>\n",
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" <tr>\n",
" <th>40</th>\n",
" <td>Remove Multicollinearity</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Multicollinearity Threshold</td>\n",
" <td>None</td>\n",
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" <tr>\n",
" <th>42</th>\n",
" <td>Remove Perfect Collinearity</td>\n",
" <td>True</td>\n",
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" <th>43</th>\n",
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" <td>False</td>\n",
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" <td>None</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>Feature Selection Method</td>\n",
" <td>classic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Features Selection Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
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" <th>53</th>\n",
" <td>Feature Interaction</td>\n",
" <td>False</td>\n",
" </tr>\n",
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" <td>False</td>\n",
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" <th>55</th>\n",
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" <td>None</td>\n",
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" <td>False</td>\n",
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" <th>57</th>\n",
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" <td>box-cox</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Description Value\n",
"0 session_id 4417\n",
"1 Target Price\n",
"2 Original Data (6000, 8)\n",
"3 Missing Values False\n",
"4 Numeric Features 1\n",
"5 Categorical Features 6\n",
"6 Ordinal Features False\n",
"7 High Cardinality Features False\n",
"8 High Cardinality Method None\n",
"9 Transformed Train Set (4199, 28)\n",
"10 Transformed Test Set (1801, 28)\n",
"11 Shuffle Train-Test True\n",
"12 Stratify Train-Test False\n",
"13 Fold Generator KFold\n",
"14 Fold Number 10\n",
"15 CPU Jobs -1\n",
"16 Use GPU False\n",
"17 Log Experiment False\n",
"18 Experiment Name reg-default-name\n",
"19 USI 79cc\n",
"20 Imputation Type simple\n",
"21 Iterative Imputation Iteration None\n",
"22 Numeric Imputer mean\n",
"23 Iterative Imputation Numeric Model None\n",
"24 Categorical Imputer constant\n",
"25 Iterative Imputation Categorical Model None\n",
"26 Unknown Categoricals Handling least_frequent\n",
"27 Normalize False\n",
"28 Normalize Method None\n",
"29 Transformation False\n",
"30 Transformation Method None\n",
"31 PCA False\n",
"32 PCA Method None\n",
"33 PCA Components None\n",
"34 Ignore Low Variance False\n",
"35 Combine Rare Levels False\n",
"36 Rare Level Threshold None\n",
"37 Numeric Binning False\n",
"38 Remove Outliers False\n",
"39 Outliers Threshold None\n",
"40 Remove Multicollinearity False\n",
"41 Multicollinearity Threshold None\n",
"42 Remove Perfect Collinearity True\n",
"43 Clustering False\n",
"44 Clustering Iteration None\n",
"45 Polynomial Features False\n",
"46 Polynomial Degree None\n",
"47 Trignometry Features False\n",
"48 Polynomial Threshold None\n",
"49 Group Features False\n",
"50 Feature Selection False\n",
"51 Feature Selection Method classic\n",
"52 Features Selection Threshold None\n",
"53 Feature Interaction False\n",
"54 Feature Ratio False\n",
"55 Interaction Threshold None\n",
"56 Transform Target False\n",
"57 Transform Target Method box-cox"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"이제 모형을 만들 차례입니다. \n",
"PyCaret는 compare_models() 이거 한 방으로 가장 잘 작동하는 모형을 찾아 줍니다!"
],
"metadata": {
"id": "MRIfbnlrgIQ3"
}
},
{
"cell_type": "code",
"source": [
"compare_models()"
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"output_type": "display_data",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" <th>TT (Sec)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>et</th>\n",
" <td>Extra Trees Regressor</td>\n",
" <td>7.253200e+02</td>\n",
" <td>2.089617e+06</td>\n",
" <td>1.412893e+03</td>\n",
" <td>9.809000e-01</td>\n",
" <td>0.0775</td>\n",
" <td>0.0584</td>\n",
" <td>1.451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rf</th>\n",
" <td>Random Forest Regressor</td>\n",
" <td>7.194286e+02</td>\n",
" <td>2.380044e+06</td>\n",
" <td>1.492959e+03</td>\n",
" <td>9.785000e-01</td>\n",
" <td>0.0778</td>\n",
" <td>0.0575</td>\n",
" <td>1.451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gbr</th>\n",
" <td>Gradient Boosting Regressor</td>\n",
" <td>9.013263e+02</td>\n",
" <td>3.084118e+06</td>\n",
" <td>1.726864e+03</td>\n",
" <td>9.717000e-01</td>\n",
" <td>0.1013</td>\n",
" <td>0.0767</td>\n",
" <td>0.302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lightgbm</th>\n",
" <td>Light Gradient Boosting Machine</td>\n",
" <td>7.622670e+02</td>\n",
" <td>3.445734e+06</td>\n",
" <td>1.771217e+03</td>\n",
" <td>9.692000e-01</td>\n",
" <td>0.0774</td>\n",
" <td>0.0562</td>\n",
" <td>0.114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dt</th>\n",
" <td>Decision Tree Regressor</td>\n",
" <td>9.402904e+02</td>\n",
" <td>3.919769e+06</td>\n",
" <td>1.943954e+03</td>\n",
" <td>9.636000e-01</td>\n",
" <td>0.1006</td>\n",
" <td>0.0737</td>\n",
" <td>0.033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ridge</th>\n",
" <td>Ridge Regression</td>\n",
" <td>2.522849e+03</td>\n",
" <td>1.553797e+07</td>\n",
" <td>3.911392e+03</td>\n",
" <td>8.557000e-01</td>\n",
" <td>0.6438</td>\n",
" <td>0.2985</td>\n",
" <td>0.038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lasso</th>\n",
" <td>Lasso Regression</td>\n",
" <td>2.519646e+03</td>\n",
" <td>1.555177e+07</td>\n",
" <td>3.913367e+03</td>\n",
" <td>8.555000e-01</td>\n",
" <td>0.6405</td>\n",
" <td>0.2977</td>\n",
" <td>0.063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>br</th>\n",
" <td>Bayesian Ridge</td>\n",
" <td>2.522436e+03</td>\n",
" <td>1.556019e+07</td>\n",
" <td>3.914523e+03</td>\n",
" <td>8.554000e-01</td>\n",
" <td>0.6393</td>\n",
" <td>0.2983</td>\n",
" <td>0.022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>llar</th>\n",
" <td>Lasso Least Angle Regression</td>\n",
" <td>2.463327e+03</td>\n",
" <td>1.559329e+07</td>\n",
" <td>3.916103e+03</td>\n",
" <td>8.553000e-01</td>\n",
" <td>0.6665</td>\n",
" <td>0.2839</td>\n",
" <td>0.020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lr</th>\n",
" <td>Linear Regression</td>\n",
" <td>2.535285e+03</td>\n",
" <td>1.558196e+07</td>\n",
" <td>3.917170e+03</td>\n",
" <td>8.552000e-01</td>\n",
" <td>0.6496</td>\n",
" <td>0.3017</td>\n",
" <td>0.598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>huber</th>\n",
" <td>Huber Regressor</td>\n",
" <td>2.003904e+03</td>\n",
" <td>2.115274e+07</td>\n",
" <td>4.544653e+03</td>\n",
" <td>8.051000e-01</td>\n",
" <td>0.4180</td>\n",
" <td>0.1688</td>\n",
" <td>0.145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>par</th>\n",
" <td>Passive Aggressive Regressor</td>\n",
" <td>2.014468e+03</td>\n",
" <td>2.291784e+07</td>\n",
" <td>4.721545e+03</td>\n",
" <td>7.896000e-01</td>\n",
" <td>0.4229</td>\n",
" <td>0.1610</td>\n",
" <td>0.051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ada</th>\n",
" <td>AdaBoost Regressor</td>\n",
" <td>4.232378e+03</td>\n",
" <td>2.571827e+07</td>\n",
" <td>5.048940e+03</td>\n",
" <td>7.565000e-01</td>\n",
" <td>0.4781</td>\n",
" <td>0.5518</td>\n",
" <td>0.263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>omp</th>\n",
" <td>Orthogonal Matching Pursuit</td>\n",
" <td>3.020520e+03</td>\n",
" <td>2.692980e+07</td>\n",
" <td>5.143359e+03</td>\n",
" <td>7.506000e-01</td>\n",
" <td>0.3995</td>\n",
" <td>0.2722</td>\n",
" <td>0.017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>knn</th>\n",
" <td>K Neighbors Regressor</td>\n",
" <td>3.041608e+03</td>\n",
" <td>3.021692e+07</td>\n",
" <td>5.488381e+03</td>\n",
" <td>7.132000e-01</td>\n",
" <td>0.3716</td>\n",
" <td>0.2806</td>\n",
" <td>0.080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>en</th>\n",
" <td>Elastic Net</td>\n",
" <td>5.100566e+03</td>\n",
" <td>6.056820e+07</td>\n",
" <td>7.746799e+03</td>\n",
" <td>4.335000e-01</td>\n",
" <td>0.5389</td>\n",
" <td>0.5845</td>\n",
" <td>0.039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dummy</th>\n",
" <td>Dummy Regressor</td>\n",
" <td>7.345813e+03</td>\n",
" <td>1.063003e+08</td>\n",
" <td>1.028637e+04</td>\n",
" <td>-1.800000e-03</td>\n",
" <td>0.7600</td>\n",
" <td>0.8936</td>\n",
" <td>0.013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lar</th>\n",
" <td>Least Angle Regression</td>\n",
" <td>2.793011e+06</td>\n",
" <td>3.301584e+14</td>\n",
" <td>5.919992e+06</td>\n",
" <td>-2.375630e+06</td>\n",
" <td>1.7392</td>\n",
" <td>380.9053</td>\n",
" <td>0.023</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model MAE MSE \\\n",
"et Extra Trees Regressor 7.253200e+02 2.089617e+06 \n",
"rf Random Forest Regressor 7.194286e+02 2.380044e+06 \n",
"gbr Gradient Boosting Regressor 9.013263e+02 3.084118e+06 \n",
"lightgbm Light Gradient Boosting Machine 7.622670e+02 3.445734e+06 \n",
"dt Decision Tree Regressor 9.402904e+02 3.919769e+06 \n",
"ridge Ridge Regression 2.522849e+03 1.553797e+07 \n",
"lasso Lasso Regression 2.519646e+03 1.555177e+07 \n",
"br Bayesian Ridge 2.522436e+03 1.556019e+07 \n",
"llar Lasso Least Angle Regression 2.463327e+03 1.559329e+07 \n",
"lr Linear Regression 2.535285e+03 1.558196e+07 \n",
"huber Huber Regressor 2.003904e+03 2.115274e+07 \n",
"par Passive Aggressive Regressor 2.014468e+03 2.291784e+07 \n",
"ada AdaBoost Regressor 4.232378e+03 2.571827e+07 \n",
"omp Orthogonal Matching Pursuit 3.020520e+03 2.692980e+07 \n",
"knn K Neighbors Regressor 3.041608e+03 3.021692e+07 \n",
"en Elastic Net 5.100566e+03 6.056820e+07 \n",
"dummy Dummy Regressor 7.345813e+03 1.063003e+08 \n",
"lar Least Angle Regression 2.793011e+06 3.301584e+14 \n",
"\n",
" RMSE R2 RMSLE MAPE TT (Sec) \n",
"et 1.412893e+03 9.809000e-01 0.0775 0.0584 1.451 \n",
"rf 1.492959e+03 9.785000e-01 0.0778 0.0575 1.451 \n",
"gbr 1.726864e+03 9.717000e-01 0.1013 0.0767 0.302 \n",
"lightgbm 1.771217e+03 9.692000e-01 0.0774 0.0562 0.114 \n",
"dt 1.943954e+03 9.636000e-01 0.1006 0.0737 0.033 \n",
"ridge 3.911392e+03 8.557000e-01 0.6438 0.2985 0.038 \n",
"lasso 3.913367e+03 8.555000e-01 0.6405 0.2977 0.063 \n",
"br 3.914523e+03 8.554000e-01 0.6393 0.2983 0.022 \n",
"llar 3.916103e+03 8.553000e-01 0.6665 0.2839 0.020 \n",
"lr 3.917170e+03 8.552000e-01 0.6496 0.3017 0.598 \n",
"huber 4.544653e+03 8.051000e-01 0.4180 0.1688 0.145 \n",
"par 4.721545e+03 7.896000e-01 0.4229 0.1610 0.051 \n",
"ada 5.048940e+03 7.565000e-01 0.4781 0.5518 0.263 \n",
"omp 5.143359e+03 7.506000e-01 0.3995 0.2722 0.017 \n",
"knn 5.488381e+03 7.132000e-01 0.3716 0.2806 0.080 \n",
"en 7.746799e+03 4.335000e-01 0.5389 0.5845 0.039 \n",
"dummy 1.028637e+04 -1.800000e-03 0.7600 0.8936 0.013 \n",
"lar 5.919992e+06 -2.375630e+06 1.7392 380.9053 0.023 "
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=100, n_jobs=-1, oob_score=False,\n",
" random_state=4417, verbose=0, warm_start=False)"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"source": [
"여러 모형들의 추론 결과가 나왔습니다. 가장 성능이 좋은 순으로 정렬이 되므로 맨 위에 표시된 Extra Trees Regressor을 사용해 보겠습니다.\n"
],
"metadata": {
"id": "EFhGloueMJuu"
}
},
{
"cell_type": "code",
"source": [
"model = create_model('et')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426,
"referenced_widgets": [
"c96575810e9645879a74caf082ec51b4",
"01a6b7ac8c0446a58ad83864a77acf9a",
"0febddd05ca14cef901a0bd6548d3f0e"
]
},
"id": "smAZZaLMgCBN",
"outputId": "bfb573a5-4e8e-44f4-cd1d-4f6c619fb26f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706.8477</td>\n",
" <td>1.851739e+06</td>\n",
" <td>1360.7860</td>\n",
" <td>0.9820</td>\n",
" <td>0.0792</td>\n",
" <td>0.0595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>714.7612</td>\n",
" <td>1.574976e+06</td>\n",
" <td>1254.9804</td>\n",
" <td>0.9833</td>\n",
" <td>0.0794</td>\n",
" <td>0.0602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>837.1378</td>\n",
" <td>3.162244e+06</td>\n",
" <td>1778.2700</td>\n",
" <td>0.9717</td>\n",
" <td>0.0836</td>\n",
" <td>0.0619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>723.5708</td>\n",
" <td>1.655954e+06</td>\n",
" <td>1286.8388</td>\n",
" <td>0.9843</td>\n",
" <td>0.0777</td>\n",
" <td>0.0569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>658.3075</td>\n",
" <td>1.331229e+06</td>\n",
" <td>1153.7888</td>\n",
" <td>0.9844</td>\n",
" <td>0.0773</td>\n",
" <td>0.0593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>829.7322</td>\n",
" <td>4.622626e+06</td>\n",
" <td>2150.0294</td>\n",
" <td>0.9667</td>\n",
" <td>0.0807</td>\n",
" <td>0.0603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>693.2810</td>\n",
" <td>1.520736e+06</td>\n",
" <td>1233.1813</td>\n",
" <td>0.9866</td>\n",
" <td>0.0731</td>\n",
" <td>0.0563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>756.1417</td>\n",
" <td>2.342081e+06</td>\n",
" <td>1530.3860</td>\n",
" <td>0.9793</td>\n",
" <td>0.0764</td>\n",
" <td>0.0574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>655.1617</td>\n",
" <td>1.369641e+06</td>\n",
" <td>1170.3167</td>\n",
" <td>0.9874</td>\n",
" <td>0.0729</td>\n",
" <td>0.0555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>678.2579</td>\n",
" <td>1.464946e+06</td>\n",
" <td>1210.3496</td>\n",
" <td>0.9832</td>\n",
" <td>0.0749</td>\n",
" <td>0.0563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>725.3200</td>\n",
" <td>2.089617e+06</td>\n",
" <td>1412.8927</td>\n",
" <td>0.9809</td>\n",
" <td>0.0775</td>\n",
" <td>0.0584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>61.2087</td>\n",
" <td>9.973598e+05</td>\n",
" <td>305.5347</td>\n",
" <td>0.0063</td>\n",
" <td>0.0032</td>\n",
" <td>0.0020</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 706.8477 1.851739e+06 1360.7860 0.9820 0.0792 0.0595\n",
"1 714.7612 1.574976e+06 1254.9804 0.9833 0.0794 0.0602\n",
"2 837.1378 3.162244e+06 1778.2700 0.9717 0.0836 0.0619\n",
"3 723.5708 1.655954e+06 1286.8388 0.9843 0.0777 0.0569\n",
"4 658.3075 1.331229e+06 1153.7888 0.9844 0.0773 0.0593\n",
"5 829.7322 4.622626e+06 2150.0294 0.9667 0.0807 0.0603\n",
"6 693.2810 1.520736e+06 1233.1813 0.9866 0.0731 0.0563\n",
"7 756.1417 2.342081e+06 1530.3860 0.9793 0.0764 0.0574\n",
"8 655.1617 1.369641e+06 1170.3167 0.9874 0.0729 0.0555\n",
"9 678.2579 1.464946e+06 1210.3496 0.9832 0.0749 0.0563\n",
"Mean 725.3200 2.089617e+06 1412.8927 0.9809 0.0775 0.0584\n",
"SD 61.2087 9.973598e+05 305.5347 0.0063 0.0032 0.0020"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"표시된 내용은 교차 검증을 10회 수행한 결과와 그 평균(Mean) 및 표준편차(Standard Deviation)입니다. \n",
"\n",
"잠시 하이퍼파라미터를 포함해 모형의 상세를 살펴보겠습니다."
],
"metadata": {
"id": "Zs6lDo2AhdDU"
}
},
{
"cell_type": "code",
"source": [
"model"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OtWcuSSIhWd5",
"outputId": "d38fc1c7-65d3-4057-942b-5b24cdcb856d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=100, n_jobs=-1, oob_score=False,\n",
" random_state=4417, verbose=0, warm_start=False)"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "markdown",
"source": [
"이제 시각화를 통해 이 잘 만들어 졌는지 확인해 보겠습니다."
],
"metadata": {
"id": "sdn1Mu4DOpNu"
}
},
{
"cell_type": "code",
"source": [
"plot_model(model)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376,
"referenced_widgets": [
"704fa978c6c04b49bc561e547da2b637",
"406a205a1c3f4e2e973777726d72da0f",
"cd2d0dd380854d108f3cca927fe05b49"
]
},
"id": "cmahKk9qOeC5",
"outputId": "50b29431-ddc2-425a-de81-c206207dff74"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 2 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"만들어진 모형을 대상으로 하이퍼파라미터 최적화를 수행해 보겠습니니다. 이 작업은 40분 정도의 시간이 걸립니다."
],
"metadata": {
"id": "O2n-LgzQOXWq"
}
},
{
"cell_type": "code",
"source": [
"tuned_model = tune_model(model)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426,
"referenced_widgets": [
"a0267b8ea717436ebc4bdf02760f8ab1",
"3e6d4b76c994492f9167a1b4ea0416f1",
"94e5d288a37f45309d7b12e820d7bb87"
]
},
"id": "QP3zgOQPhi8Q",
"outputId": "7fb0e67d-55b8-4983-a4ad-a7531b13f6f4"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>796.6646</td>\n",
" <td>2.933526e+06</td>\n",
" <td>1712.7540</td>\n",
" <td>0.9716</td>\n",
" <td>0.0876</td>\n",
" <td>0.0675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>745.6158</td>\n",
" <td>1.720768e+06</td>\n",
" <td>1311.7806</td>\n",
" <td>0.9818</td>\n",
" <td>0.0824</td>\n",
" <td>0.0622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>867.8531</td>\n",
" <td>5.487956e+06</td>\n",
" <td>2342.6386</td>\n",
" <td>0.9508</td>\n",
" <td>0.0877</td>\n",
" <td>0.0637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>924.7672</td>\n",
" <td>4.513364e+06</td>\n",
" <td>2124.4678</td>\n",
" <td>0.9573</td>\n",
" <td>0.0924</td>\n",
" <td>0.0669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>708.7673</td>\n",
" <td>1.937939e+06</td>\n",
" <td>1392.0986</td>\n",
" <td>0.9772</td>\n",
" <td>0.0854</td>\n",
" <td>0.0652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>978.4641</td>\n",
" <td>8.216249e+06</td>\n",
" <td>2866.4001</td>\n",
" <td>0.9409</td>\n",
" <td>0.0911</td>\n",
" <td>0.0679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>732.4930</td>\n",
" <td>2.022965e+06</td>\n",
" <td>1422.3099</td>\n",
" <td>0.9821</td>\n",
" <td>0.0750</td>\n",
" <td>0.0583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>934.0893</td>\n",
" <td>4.480752e+06</td>\n",
" <td>2116.7787</td>\n",
" <td>0.9603</td>\n",
" <td>0.0926</td>\n",
" <td>0.0702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>751.4517</td>\n",
" <td>1.965643e+06</td>\n",
" <td>1402.0137</td>\n",
" <td>0.9819</td>\n",
" <td>0.0859</td>\n",
" <td>0.0656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>774.1197</td>\n",
" <td>1.587920e+06</td>\n",
" <td>1260.1269</td>\n",
" <td>0.9818</td>\n",
" <td>0.0854</td>\n",
" <td>0.0673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>821.4286</td>\n",
" <td>3.486708e+06</td>\n",
" <td>1795.1369</td>\n",
" <td>0.9686</td>\n",
" <td>0.0866</td>\n",
" <td>0.0655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>91.8066</td>\n",
" <td>2.056709e+06</td>\n",
" <td>513.9958</td>\n",
" <td>0.0144</td>\n",
" <td>0.0050</td>\n",
" <td>0.0032</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 796.6646 2.933526e+06 1712.7540 0.9716 0.0876 0.0675\n",
"1 745.6158 1.720768e+06 1311.7806 0.9818 0.0824 0.0622\n",
"2 867.8531 5.487956e+06 2342.6386 0.9508 0.0877 0.0637\n",
"3 924.7672 4.513364e+06 2124.4678 0.9573 0.0924 0.0669\n",
"4 708.7673 1.937939e+06 1392.0986 0.9772 0.0854 0.0652\n",
"5 978.4641 8.216249e+06 2866.4001 0.9409 0.0911 0.0679\n",
"6 732.4930 2.022965e+06 1422.3099 0.9821 0.0750 0.0583\n",
"7 934.0893 4.480752e+06 2116.7787 0.9603 0.0926 0.0702\n",
"8 751.4517 1.965643e+06 1402.0137 0.9819 0.0859 0.0656\n",
"9 774.1197 1.587920e+06 1260.1269 0.9818 0.0854 0.0673\n",
"Mean 821.4286 3.486708e+06 1795.1369 0.9686 0.0866 0.0655\n",
"SD 91.8066 2.056709e+06 513.9958 0.0144 0.0050 0.0032"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"최적화가 수행된 이후의 하이퍼파미터의 내용을 살펴봅시다."
],
"metadata": {
"id": "f0vpQnVcOLFU"
}
},
{
"cell_type": "code",
"source": [
"tuned_model"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5FKgWGxpxI_i",
"outputId": "7fe30185-912e-4099-cd4f-443abda953eb"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ExtraTreesRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=10, max_features=1.0, max_leaf_nodes=None,\n",
" max_samples=None, min_impurity_decrease=0.0002,\n",
" min_impurity_split=None, min_samples_leaf=4,\n",
" min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
" n_estimators=250, n_jobs=-1, oob_score=False,\n",
" random_state=4417, verbose=0, warm_start=False)"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"source": [
"이제 모형을 이용해 예측한 결과와 실제 가격을 비교해 봅시다. Price가 실제 가격이고 Label은 모형이 추론한 가격 입니다."
],
"metadata": {
"id": "0-NfoSzOPCoR"
}
},
{
"cell_type": "code",
"source": [
"predict_model(tuned_model)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 525
},
"id": "RAf7rQjqmIUm",
"outputId": "2390b05d-ed46-4f2c-81eb-61f139a0c59b"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Extra Trees Regressor</td>\n",
" <td>802.8863</td>\n",
" <td>1.913203e+06</td>\n",
" <td>1383.1859</td>\n",
" <td>0.9804</td>\n",
" <td>0.0887</td>\n",
" <td>0.0678</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model MAE MSE ... R2 RMSLE MAPE\n",
"0 Extra Trees Regressor 802.8863 1.913203e+06 ... 0.9804 0.0887 0.0678\n",
"\n",
"[1 rows x 7 columns]"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Carat Weight</th>\n",
" <th>Cut_Fair</th>\n",
" <th>Cut_Good</th>\n",
" <th>Cut_Ideal</th>\n",
" <th>Cut_Signature-Ideal</th>\n",
" <th>Cut_Very Good</th>\n",
" <th>Color_D</th>\n",
" <th>Color_E</th>\n",
" <th>Color_F</th>\n",
" <th>Color_G</th>\n",
" <th>Color_H</th>\n",
" <th>Color_I</th>\n",
" <th>Clarity_FL</th>\n",
" <th>Clarity_IF</th>\n",
" <th>Clarity_SI1</th>\n",
" <th>Clarity_VS1</th>\n",
" <th>Clarity_VS2</th>\n",
" <th>Clarity_VVS1</th>\n",
" <th>Clarity_VVS2</th>\n",
" <th>Polish_EX</th>\n",
" <th>Polish_G</th>\n",
" <th>Polish_ID</th>\n",
" <th>Polish_VG</th>\n",
" <th>Symmetry_EX</th>\n",
" <th>Symmetry_G</th>\n",
" <th>Symmetry_ID</th>\n",
" <th>Symmetry_VG</th>\n",
" <th>Report_GIA</th>\n",
" <th>Price</th>\n",
" <th>Label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.01</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>6825</td>\n",
" <td>7320.343803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.61</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>16205</td>\n",
" <td>16308.234711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.17</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>25707</td>\n",
" <td>25670.884109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.21</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7810</td>\n",
" <td>7237.169775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.91</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3525</td>\n",
" <td>3841.674563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1796</th>\n",
" <td>1.32</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>9513</td>\n",
" <td>9058.079597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1797</th>\n",
" <td>0.79</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2888</td>\n",
" <td>2916.601843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1798</th>\n",
" <td>1.06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6003</td>\n",
" <td>5460.893484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1799</th>\n",
" <td>1.19</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>6335</td>\n",
" <td>6428.452253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1800</th>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3525</td>\n",
" <td>3712.824582</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1801 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" Carat Weight Cut_Fair Cut_Good ... Report_GIA Price Label\n",
"0 1.01 0.0 1.0 ... 1.0 6825 7320.343803\n",
"1 1.61 0.0 0.0 ... 1.0 16205 16308.234711\n",
"2 2.17 0.0 0.0 ... 1.0 25707 25670.884109\n",
"3 1.21 0.0 0.0 ... 0.0 7810 7237.169775\n",
"4 0.91 0.0 0.0 ... 1.0 3525 3841.674563\n",
"... ... ... ... ... ... ... ...\n",
"1796 1.32 0.0 0.0 ... 1.0 9513 9058.079597\n",
"1797 0.79 0.0 0.0 ... 1.0 2888 2916.601843\n",
"1798 1.06 0.0 0.0 ... 1.0 6003 5460.893484\n",
"1799 1.19 0.0 0.0 ... 1.0 6335 6428.452253\n",
"1800 0.75 0.0 0.0 ... 0.0 3525 3712.824582\n",
"\n",
"[1801 rows x 30 columns]"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"source": [
"예측치와 실제 가격의 차이가 크진 않은것 같지만 눈으로 파악하기는 어렵습니다. \n",
"아까와 마찬가지로 시각화를 통해 모형의 성능을 확인해 보겠습니다."
],
"metadata": {
"id": "AdeicLiW135T"
}
},
{
"cell_type": "code",
"source": [
"plot_model(tuned_model)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376,
"referenced_widgets": [
"9f84f9037515423383eaba950fd5dcfc",
"80611e8365a147dd92e4c2050fb9f86b",
"7b21858d2e4947fc838f122705b19ae1"
]
},
"id": "6aha5m-YvjL9",
"outputId": "08bbe356-0229-4e32-b9d6-5e737ca41561"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 576x396 with 2 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"파란색 점은 학습에 의해 예측된 값이고 녹색은 테스트에 의해 예측된 값 입니다. 0에 가까울 수록 좀 더 정확하게 맞췄다고 볼 수 있는데 튜닝 후에 좀 더 분산이 좋아진 것을 알 수 있습니다. \n",
"그럼 어떤 특성이 가격에 가장 큰 영향을 미쳤는지 살펴봅시다. plot_model에 plot='feature'를 추가해 주는것 만으로 끝납니다."
],
"metadata": {
"id": "Hv9aL08i2Ma0"
}
},
{
"cell_type": "code",
"source": [
"plot_model(tuned_model, plot='feature')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 478,
"referenced_widgets": [
"7d6a997e72454d6e9010793540ba9ead",
"bed1185cf2694996ba4f382336054777",
"e57e4eb9160d4879be00834a1baca394"
]
},
"id": "ljGaNg8LzJXB",
"outputId": "d4e99ec8-b5b6-4056-e942-439c5e9b8c50"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"어떻습니까? 정말 몇 줄 안되는 코드로 모형을 만들고 검증까지 수행했습니다.\n"
],
"metadata": {
"id": "sqouKcNuRylC"
}
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "J3srB-h3auA8"
},
"execution_count": null,
"outputs": []
}
]
}
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