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pikonha/pokemon-automl-analysis.ipynb

Created April 9, 2021 00:15
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pokemon-automl-analysis.ipynb
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pokemon-automl-analysis.ipynb
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"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 89
},
"id": "xvcPgn61DaP4",
"outputId": "9a899290-6f76-463f-d615-73da4d4e5e5f"
},
"source": [
"from google.colab import files\n",
"\n",
"files.upload() #upload kaggle.json\n",
"\n",
"!pip install -q kaggle\n",
"!mkdir -p ~/.kaggle\n",
"!cp kaggle.json ~/.kaggle/\n",
"!ls ~/.kaggle\n",
"!chmod 600 /root/.kaggle/kaggle.json\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-367b2cd3-576f-4540-a8d1-3ea5b7bcb068\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-367b2cd3-576f-4540-a8d1-3ea5b7bcb068\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving kaggle.json to kaggle.json\n",
"kaggle.json\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AMBB2sZWERrE",
"outputId": "0615d63b-8d5c-45d5-c54f-ac0daea9d0a1"
},
"source": [
"!kaggle datasets download -d rounakbanik/pokemon\n",
"!unzip pokemon.zip -d data"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"pokemon.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
"Archive: pokemon.zip\n",
" inflating: data/pokemon.csv \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Kpbwhhx-EtP4"
},
"source": [
"import pandas as pd"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 343
},
"id": "h27EjxgwEv-8",
"outputId": "0e46c216-f352-443e-c3d7-b7ae3ca189c9"
},
"source": [
"df = pd.read_csv('data/pokemon.csv')\n",
"df.head()"
],
"execution_count": null,
"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>abilities</th>\n",
" <th>against_bug</th>\n",
" <th>against_dark</th>\n",
" <th>against_dragon</th>\n",
" <th>against_electric</th>\n",
" <th>against_fairy</th>\n",
" <th>against_fight</th>\n",
" <th>against_fire</th>\n",
" <th>against_flying</th>\n",
" <th>against_ghost</th>\n",
" <th>against_grass</th>\n",
" <th>against_ground</th>\n",
" <th>against_ice</th>\n",
" <th>against_normal</th>\n",
" <th>against_poison</th>\n",
" <th>against_psychic</th>\n",
" <th>against_rock</th>\n",
" <th>against_steel</th>\n",
" <th>against_water</th>\n",
" <th>attack</th>\n",
" <th>base_egg_steps</th>\n",
" <th>base_happiness</th>\n",
" <th>base_total</th>\n",
" <th>capture_rate</th>\n",
" <th>classfication</th>\n",
" <th>defense</th>\n",
" <th>experience_growth</th>\n",
" <th>height_m</th>\n",
" <th>hp</th>\n",
" <th>japanese_name</th>\n",
" <th>name</th>\n",
" <th>percentage_male</th>\n",
" <th>pokedex_number</th>\n",
" <th>sp_attack</th>\n",
" <th>sp_defense</th>\n",
" <th>speed</th>\n",
" <th>type1</th>\n",
" <th>type2</th>\n",
" <th>weight_kg</th>\n",
" <th>generation</th>\n",
" <th>is_legendary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>['Overgrow', 'Chlorophyll']</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>49</td>\n",
" <td>5120</td>\n",
" <td>70</td>\n",
" <td>318</td>\n",
" <td>45</td>\n",
" <td>Seed Pokémon</td>\n",
" <td>49</td>\n",
" <td>1059860</td>\n",
" <td>0.7</td>\n",
" <td>45</td>\n",
" <td>Fushigidaneフシギダネ</td>\n",
" <td>Bulbasaur</td>\n",
" <td>88.1</td>\n",
" <td>1</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>45</td>\n",
" <td>grass</td>\n",
" <td>poison</td>\n",
" <td>6.9</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>['Overgrow', 'Chlorophyll']</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>62</td>\n",
" <td>5120</td>\n",
" <td>70</td>\n",
" <td>405</td>\n",
" <td>45</td>\n",
" <td>Seed Pokémon</td>\n",
" <td>63</td>\n",
" <td>1059860</td>\n",
" <td>1.0</td>\n",
" <td>60</td>\n",
" <td>Fushigisouフシギソウ</td>\n",
" <td>Ivysaur</td>\n",
" <td>88.1</td>\n",
" <td>2</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>60</td>\n",
" <td>grass</td>\n",
" <td>poison</td>\n",
" <td>13.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>['Overgrow', 'Chlorophyll']</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>100</td>\n",
" <td>5120</td>\n",
" <td>70</td>\n",
" <td>625</td>\n",
" <td>45</td>\n",
" <td>Seed Pokémon</td>\n",
" <td>123</td>\n",
" <td>1059860</td>\n",
" <td>2.0</td>\n",
" <td>80</td>\n",
" <td>Fushigibanaフシギバナ</td>\n",
" <td>Venusaur</td>\n",
" <td>88.1</td>\n",
" <td>3</td>\n",
" <td>122</td>\n",
" <td>120</td>\n",
" <td>80</td>\n",
" <td>grass</td>\n",
" <td>poison</td>\n",
" <td>100.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>['Blaze', 'Solar Power']</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>52</td>\n",
" <td>5120</td>\n",
" <td>70</td>\n",
" <td>309</td>\n",
" <td>45</td>\n",
" <td>Lizard Pokémon</td>\n",
" <td>43</td>\n",
" <td>1059860</td>\n",
" <td>0.6</td>\n",
" <td>39</td>\n",
" <td>Hitokageヒトカゲ</td>\n",
" <td>Charmander</td>\n",
" <td>88.1</td>\n",
" <td>4</td>\n",
" <td>60</td>\n",
" <td>50</td>\n",
" <td>65</td>\n",
" <td>fire</td>\n",
" <td>NaN</td>\n",
" <td>8.5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>['Blaze', 'Solar Power']</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>64</td>\n",
" <td>5120</td>\n",
" <td>70</td>\n",
" <td>405</td>\n",
" <td>45</td>\n",
" <td>Flame Pokémon</td>\n",
" <td>58</td>\n",
" <td>1059860</td>\n",
" <td>1.1</td>\n",
" <td>58</td>\n",
" <td>Lizardoリザード</td>\n",
" <td>Charmeleon</td>\n",
" <td>88.1</td>\n",
" <td>5</td>\n",
" <td>80</td>\n",
" <td>65</td>\n",
" <td>80</td>\n",
" <td>fire</td>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" abilities against_bug ... generation is_legendary\n",
"0 ['Overgrow', 'Chlorophyll'] 1.0 ... 1 0\n",
"1 ['Overgrow', 'Chlorophyll'] 1.0 ... 1 0\n",
"2 ['Overgrow', 'Chlorophyll'] 1.0 ... 1 0\n",
"3 ['Blaze', 'Solar Power'] 0.5 ... 1 0\n",
"4 ['Blaze', 'Solar Power'] 0.5 ... 1 0\n",
"\n",
"[5 rows x 41 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iFgRgoUcFE2O"
},
"source": [
"!pip install pycaret"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nofwDVYfFID4"
},
"source": [
"from pycaret import classification"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zJ3HwnJRHCUQ"
},
"source": [
"to_remove = ['abilities','japanese_name', 'name', 'pokedex_number', 'generation']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"4360b076c99b4456a8cc8ddc19abcb94",
"c7dbe09604544eeeb89c45bcf8b6830b",
"a245a2c38031455dae65d0e5e123ebd5",
"e5c266fd397e4163ada67e48c68257a3",
"c77ca78789b44482bbe9d9247e8cf30d",
"e7733f8453db438e905481ffb1e9878f"
]
},
"id": "4WuWqMw6FLrG",
"outputId": "8233ca60-aa84-498b-fdeb-123eb010f916"
},
"source": [
"ml_setup = classification.setup(data=df, \n",
" target='is_legendary', \n",
" ignore_features=to_remove,\n",
" train_size=0.8, \n",
" session_id=1234\n",
" )"
],
"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>Description</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>session_id</td>\n",
" <td>1234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Target</td>\n",
" <td>is_legendary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Target Type</td>\n",
" <td>Binary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Label Encoded</td>\n",
" <td>0: 0, 1: 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Original Data</td>\n",
" <td>(801, 41)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Missing Values</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Numeric Features</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Categorical Features</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Ordinal Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>High Cardinality Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>High Cardinality Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Transformed Train Set</td>\n",
" <td>(640, 607)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Transformed Test Set</td>\n",
" <td>(161, 607)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Shuffle Train-Test</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Stratify Train-Test</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Fold Generator</td>\n",
" <td>StratifiedKFold</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Fold Number</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>CPU Jobs</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Use GPU</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Log Experiment</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Experiment Name</td>\n",
" <td>clf-default-name</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>USI</td>\n",
" <td>7236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Imputation Type</td>\n",
" <td>simple</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Iterative Imputation Iteration</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Numeric Imputer</td>\n",
" <td>mean</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Iterative Imputation Numeric Model</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Categorical Imputer</td>\n",
" <td>constant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Iterative Imputation Categorical Model</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Unknown Categoricals Handling</td>\n",
" <td>least_frequent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Normalize</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Normalize Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Transformation</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Transformation Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>PCA</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>PCA Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>PCA Components</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Ignore Low Variance</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Combine Rare Levels</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Rare Level Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Numeric Binning</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Remove Outliers</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Outliers Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Remove Multicollinearity</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Multicollinearity Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Clustering</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Clustering Iteration</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Polynomial Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Polynomial Degree</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Trignometry Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Polynomial Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Group Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Feature Selection</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Feature Selection Method</td>\n",
" <td>classic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Features Selection Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Feature Interaction</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Feature Ratio</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>Interaction Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>Fix Imbalance</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>Fix Imbalance Method</td>\n",
" <td>SMOTE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Description Value\n",
"0 session_id 1234\n",
"1 Target is_legendary\n",
"2 Target Type Binary\n",
"3 Label Encoded 0: 0, 1: 1\n",
"4 Original Data (801, 41)\n",
"5 Missing Values True\n",
"6 Numeric Features 28\n",
"7 Categorical Features 7\n",
"8 Ordinal Features False\n",
"9 High Cardinality Features False\n",
"10 High Cardinality Method None\n",
"11 Transformed Train Set (640, 607)\n",
"12 Transformed Test Set (161, 607)\n",
"13 Shuffle Train-Test True\n",
"14 Stratify Train-Test False\n",
"15 Fold Generator StratifiedKFold\n",
"16 Fold Number 10\n",
"17 CPU Jobs -1\n",
"18 Use GPU False\n",
"19 Log Experiment False\n",
"20 Experiment Name clf-default-name\n",
"21 USI 7236\n",
"22 Imputation Type simple\n",
"23 Iterative Imputation Iteration None\n",
"24 Numeric Imputer mean\n",
"25 Iterative Imputation Numeric Model None\n",
"26 Categorical Imputer constant\n",
"27 Iterative Imputation Categorical Model None\n",
"28 Unknown Categoricals Handling least_frequent\n",
"29 Normalize False\n",
"30 Normalize Method None\n",
"31 Transformation False\n",
"32 Transformation Method None\n",
"33 PCA False\n",
"34 PCA Method None\n",
"35 PCA Components None\n",
"36 Ignore Low Variance False\n",
"37 Combine Rare Levels False\n",
"38 Rare Level Threshold None\n",
"39 Numeric Binning False\n",
"40 Remove Outliers False\n",
"41 Outliers Threshold None\n",
"42 Remove Multicollinearity False\n",
"43 Multicollinearity Threshold None\n",
"44 Clustering False\n",
"45 Clustering Iteration None\n",
"46 Polynomial Features False\n",
"47 Polynomial Degree None\n",
"48 Trignometry Features False\n",
"49 Polynomial Threshold None\n",
"50 Group Features False\n",
"51 Feature Selection False\n",
"52 Feature Selection Method classic\n",
"53 Features Selection Threshold None\n",
"54 Feature Interaction False\n",
"55 Feature Ratio False\n",
"56 Interaction Threshold None\n",
"57 Fix Imbalance False\n",
"58 Fix Imbalance Method SMOTE"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 452,
"referenced_widgets": [
"6562263f792a4b888502d8eda33f591f",
"fe9ec410a5ef405a89e7cc4c830b8039",
"5648f2e05adb4291a8cbae3a69c9f256"
]
},
"id": "Z_cfHylkJ4bn",
"outputId": "f97e1c22-49f9-4da5-c859-8b9307caf41a"
},
"source": [
"best_model = classification.compare_models(fold=5)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
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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>Accuracy</th>\n",
" <th>AUC</th>\n",
" <th>Recall</th>\n",
" <th>Prec.</th>\n",
" <th>F1</th>\n",
" <th>Kappa</th>\n",
" <th>MCC</th>\n",
" <th>TT (Sec)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>dt</th>\n",
" <td>Decision Tree Classifier</td>\n",
" <td>0.9984</td>\n",
" <td>0.9991</td>\n",
" <td>1.0000</td>\n",
" <td>0.9846</td>\n",
" <td>0.9920</td>\n",
" <td>0.9911</td>\n",
" <td>0.9913</td>\n",
" <td>0.056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>et</th>\n",
" <td>Extra Trees Classifier</td>\n",
" <td>0.9969</td>\n",
" <td>0.9991</td>\n",
" <td>0.9833</td>\n",
" <td>0.9846</td>\n",
" <td>0.9833</td>\n",
" <td>0.9816</td>\n",
" <td>0.9820</td>\n",
" <td>0.582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rf</th>\n",
" <td>Random Forest Classifier</td>\n",
" <td>0.9953</td>\n",
" <td>0.9999</td>\n",
" <td>0.9667</td>\n",
" <td>0.9846</td>\n",
" <td>0.9738</td>\n",
" <td>0.9713</td>\n",
" <td>0.9723</td>\n",
" <td>0.646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gbc</th>\n",
" <td>Gradient Boosting Classifier</td>\n",
" <td>0.9953</td>\n",
" <td>0.9991</td>\n",
" <td>0.9667</td>\n",
" <td>0.9846</td>\n",
" <td>0.9738</td>\n",
" <td>0.9713</td>\n",
" <td>0.9723</td>\n",
" <td>0.420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lightgbm</th>\n",
" <td>Light Gradient Boosting Machine</td>\n",
" <td>0.9953</td>\n",
" <td>1.0000</td>\n",
" <td>0.9667</td>\n",
" <td>0.9846</td>\n",
" <td>0.9738</td>\n",
" <td>0.9713</td>\n",
" <td>0.9723</td>\n",
" <td>0.206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ridge</th>\n",
" <td>Ridge Classifier</td>\n",
" <td>0.9938</td>\n",
" <td>0.0000</td>\n",
" <td>0.9513</td>\n",
" <td>0.9846</td>\n",
" <td>0.9666</td>\n",
" <td>0.9632</td>\n",
" <td>0.9640</td>\n",
" <td>0.084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ada</th>\n",
" <td>Ada Boost Classifier</td>\n",
" <td>0.9938</td>\n",
" <td>0.9989</td>\n",
" <td>0.9500</td>\n",
" <td>0.9846</td>\n",
" <td>0.9651</td>\n",
" <td>0.9617</td>\n",
" <td>0.9630</td>\n",
" <td>0.224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lr</th>\n",
" <td>Logistic Regression</td>\n",
" <td>0.9906</td>\n",
" <td>0.9928</td>\n",
" <td>0.9346</td>\n",
" <td>0.9692</td>\n",
" <td>0.9497</td>\n",
" <td>0.9446</td>\n",
" <td>0.9459</td>\n",
" <td>0.986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>knn</th>\n",
" <td>K Neighbors Classifier</td>\n",
" <td>0.9516</td>\n",
" <td>0.9652</td>\n",
" <td>0.7538</td>\n",
" <td>0.7463</td>\n",
" <td>0.7468</td>\n",
" <td>0.7201</td>\n",
" <td>0.7221</td>\n",
" <td>0.188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nb</th>\n",
" <td>Naive Bayes</td>\n",
" <td>0.9500</td>\n",
" <td>0.8840</td>\n",
" <td>0.8026</td>\n",
" <td>0.7236</td>\n",
" <td>0.7558</td>\n",
" <td>0.7283</td>\n",
" <td>0.7326</td>\n",
" <td>0.060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>svm</th>\n",
" <td>SVM - Linear Kernel</td>\n",
" <td>0.9031</td>\n",
" <td>0.0000</td>\n",
" <td>0.2167</td>\n",
" <td>0.3867</td>\n",
" <td>0.2220</td>\n",
" <td>0.1953</td>\n",
" <td>0.2203</td>\n",
" <td>0.064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lda</th>\n",
" <td>Linear Discriminant Analysis</td>\n",
" <td>0.7703</td>\n",
" <td>0.6956</td>\n",
" <td>0.5897</td>\n",
" <td>0.2350</td>\n",
" <td>0.3338</td>\n",
" <td>0.2277</td>\n",
" <td>0.2628</td>\n",
" <td>0.208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>qda</th>\n",
" <td>Quadratic Discriminant Analysis</td>\n",
" <td>0.0953</td>\n",
" <td>0.5000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0953</td>\n",
" <td>0.1740</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.132</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Accuracy AUC Recall Prec. \\\n",
"dt Decision Tree Classifier 0.9984 0.9991 1.0000 0.9846 \n",
"et Extra Trees Classifier 0.9969 0.9991 0.9833 0.9846 \n",
"rf Random Forest Classifier 0.9953 0.9999 0.9667 0.9846 \n",
"gbc Gradient Boosting Classifier 0.9953 0.9991 0.9667 0.9846 \n",
"lightgbm Light Gradient Boosting Machine 0.9953 1.0000 0.9667 0.9846 \n",
"ridge Ridge Classifier 0.9938 0.0000 0.9513 0.9846 \n",
"ada Ada Boost Classifier 0.9938 0.9989 0.9500 0.9846 \n",
"lr Logistic Regression 0.9906 0.9928 0.9346 0.9692 \n",
"knn K Neighbors Classifier 0.9516 0.9652 0.7538 0.7463 \n",
"nb Naive Bayes 0.9500 0.8840 0.8026 0.7236 \n",
"svm SVM - Linear Kernel 0.9031 0.0000 0.2167 0.3867 \n",
"lda Linear Discriminant Analysis 0.7703 0.6956 0.5897 0.2350 \n",
"qda Quadratic Discriminant Analysis 0.0953 0.5000 1.0000 0.0953 \n",
"\n",
" F1 Kappa MCC TT (Sec) \n",
"dt 0.9920 0.9911 0.9913 0.056 \n",
"et 0.9833 0.9816 0.9820 0.582 \n",
"rf 0.9738 0.9713 0.9723 0.646 \n",
"gbc 0.9738 0.9713 0.9723 0.420 \n",
"lightgbm 0.9738 0.9713 0.9723 0.206 \n",
"ridge 0.9666 0.9632 0.9640 0.084 \n",
"ada 0.9651 0.9617 0.9630 0.224 \n",
"lr 0.9497 0.9446 0.9459 0.986 \n",
"knn 0.7468 0.7201 0.7221 0.188 \n",
"nb 0.7558 0.7283 0.7326 0.060 \n",
"svm 0.2220 0.1953 0.2203 0.064 \n",
"lda 0.3338 0.2277 0.2628 0.208 \n",
"qda 0.1740 0.0000 0.0000 0.132 "
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 502
},
"id": "gbag8T7hN8tg",
"outputId": "b8d72bf6-d760-4b73-ed40-63d9febc5154"
},
"source": [
"classification.predict_model(best_model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Accuracy</th>\n",
" <th>AUC</th>\n",
" <th>Recall</th>\n",
" <th>Prec.</th>\n",
" <th>F1</th>\n",
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" <th>0</th>\n",
" <td>Decision Tree Classifier</td>\n",
" <td>0.9876</td>\n",
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],
"text/plain": [
" Model Accuracy AUC ... F1 Kappa MCC\n",
"0 Decision Tree Classifier 0.9876 0.9934 ... 0.9 0.8934 0.8986\n",
"\n",
"[1 rows x 8 columns]"
]
},
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" <th></th>\n",
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" <th>base_total</th>\n",
" <th>defense</th>\n",
" <th>height_m</th>\n",
" <th>hp</th>\n",
" <th>percentage_male</th>\n",
" <th>sp_attack</th>\n",
" <th>sp_defense</th>\n",
" <th>speed</th>\n",
" <th>weight_kg</th>\n",
" <th>base_egg_steps_10240</th>\n",
" <th>base_egg_steps_20480</th>\n",
" <th>base_egg_steps_2560</th>\n",
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" <th>base_egg_steps_3840</th>\n",
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" <th>base_egg_steps_6400</th>\n",
" <th>base_egg_steps_7680</th>\n",
" <th>base_egg_steps_8960</th>\n",
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" <th>type1_rock</th>\n",
" <th>type1_steel</th>\n",
" <th>type1_water</th>\n",
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" <th>type2_dark</th>\n",
" <th>type2_dragon</th>\n",
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" <th>type2_fairy</th>\n",
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" <th>Label</th>\n",
" <th>Score</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0.50</td>\n",
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" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.00</td>\n",
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" <td>2.0</td>\n",
" <td>2.0</td>\n",
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" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>65.0</td>\n",
" <td>390.0</td>\n",
" <td>45.0</td>\n",
" <td>1.2</td>\n",
" <td>65.0</td>\n",
" <td>50.000000</td>\n",
" <td>75.0</td>\n",
" <td>45.0</td>\n",
" <td>95.0</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
" <td>2.00</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>1.00</td>\n",
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" <td>80.0</td>\n",
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" <td>50.0</td>\n",
" <td>0.8</td>\n",
" <td>70.0</td>\n",
" <td>75.400002</td>\n",
" <td>35.0</td>\n",
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" <td>35.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <th>2</th>\n",
" <td>0.25</td>\n",
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" <td>1.0</td>\n",
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" <td>0.00</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.50</td>\n",
" <td>2.0</td>\n",
" <td>0.50</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.50</td>\n",
" <td>2.0</td>\n",
" <td>30.0</td>\n",
" <td>275.0</td>\n",
" <td>55.0</td>\n",
" <td>0.3</td>\n",
" <td>50.0</td>\n",
" <td>50.000000</td>\n",
" <td>65.0</td>\n",
" <td>55.0</td>\n",
" <td>20.0</td>\n",
" <td>3.100000</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>...</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>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>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>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.00</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>0.5</td>\n",
" <td>65.0</td>\n",
" <td>385.0</td>\n",
" <td>65.0</td>\n",
" <td>1.0</td>\n",
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" <td>50.000000</td>\n",
" <td>50.0</td>\n",
" <td>50.0</td>\n",
" <td>90.0</td>\n",
" <td>20.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <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",
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" <td>0.0</td>\n",
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" <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>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>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>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</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.50</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.00</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>100.0</td>\n",
" <td>465.0</td>\n",
" <td>85.0</td>\n",
" <td>1.3</td>\n",
" <td>120.0</td>\n",
" <td>100.000000</td>\n",
" <td>30.0</td>\n",
" <td>85.0</td>\n",
" <td>45.0</td>\n",
" <td>55.500000</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>...</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>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>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>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</td>\n",
" <td>0</td>\n",
" <td>1.0</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",
" <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",
" <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>156</th>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>2.00</td>\n",
" <td>2.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.00</td>\n",
" <td>0.5</td>\n",
" <td>120.0</td>\n",
" <td>700.0</td>\n",
" <td>90.0</td>\n",
" <td>3.0</td>\n",
" <td>125.0</td>\n",
" <td>55.543827</td>\n",
" <td>170.0</td>\n",
" <td>100.0</td>\n",
" <td>95.0</td>\n",
" <td>325.000000</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>...</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>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>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>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</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>157</th>\n",
" <td>2.00</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>0.50</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>73.0</td>\n",
" <td>483.0</td>\n",
" <td>70.0</td>\n",
" <td>1.6</td>\n",
" <td>85.0</td>\n",
" <td>50.000000</td>\n",
" <td>73.0</td>\n",
" <td>115.0</td>\n",
" <td>67.0</td>\n",
" <td>75.599998</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>...</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>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>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>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</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>2.00</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>4.0</td>\n",
" <td>0.25</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>2.0</td>\n",
" <td>90.0</td>\n",
" <td>600.0</td>\n",
" <td>106.0</td>\n",
" <td>1.7</td>\n",
" <td>91.0</td>\n",
" <td>50.000000</td>\n",
" <td>130.0</td>\n",
" <td>106.0</td>\n",
" <td>77.0</td>\n",
" <td>430.000000</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>...</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>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>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>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>1</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>2.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>0.5</td>\n",
" <td>50.0</td>\n",
" <td>320.0</td>\n",
" <td>55.0</td>\n",
" <td>0.5</td>\n",
" <td>45.0</td>\n",
" <td>50.000000</td>\n",
" <td>75.0</td>\n",
" <td>65.0</td>\n",
" <td>30.0</td>\n",
" <td>5.400000</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>...</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>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>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>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</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>2.00</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>0.50</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>25.0</td>\n",
" <td>330.0</td>\n",
" <td>35.0</td>\n",
" <td>0.7</td>\n",
" <td>60.0</td>\n",
" <td>50.000000</td>\n",
" <td>70.0</td>\n",
" <td>80.0</td>\n",
" <td>60.0</td>\n",
" <td>30.600000</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>...</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>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>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>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</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>161 rows × 610 columns</p>\n",
"</div>"
],
"text/plain": [
" against_bug against_dark against_dragon ... is_legendary Label Score\n",
"0 0.50 1.0 1.0 ... 0 0 1.0\n",
"1 0.50 0.5 1.0 ... 0 0 1.0\n",
"2 0.25 2.0 1.0 ... 0 0 1.0\n",
"3 1.00 1.0 1.0 ... 0 0 1.0\n",
"4 0.50 0.5 1.0 ... 0 0 1.0\n",
".. ... ... ... ... ... ... ...\n",
"156 1.00 1.0 2.0 ... 1 1 1.0\n",
"157 2.00 2.0 1.0 ... 0 0 1.0\n",
"158 0.25 1.0 0.5 ... 1 1 1.0\n",
"159 1.00 1.0 1.0 ... 0 0 1.0\n",
"160 2.00 2.0 1.0 ... 0 0 1.0\n",
"\n",
"[161 rows x 610 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 492,
"referenced_widgets": [
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},
"id": "j9Zy4ehCPqzw",
"outputId": "e441df4e-8780-4900-d414-dd22d0d2a062"
},
"source": [
"classification.evaluate_model(best_model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8ff9be4cd1c74273a4108d8b36cdbe44",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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