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June 7, 2021 21:47
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| sepal.length | sepal.width | petal.length | petal.width | variety | |
|---|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | .2 | Setosa | |
| 4.9 | 3 | 1.4 | .2 | Setosa | |
| 4.7 | 3.2 | 1.3 | .2 | Setosa | |
| 4.6 | 3.1 | 1.5 | .2 | Setosa | |
| 5 | 3.6 | 1.4 | .2 | Setosa | |
| 5.4 | 3.9 | 1.7 | .4 | Setosa | |
| 4.6 | 3.4 | 1.4 | .3 | Setosa | |
| 5 | 3.4 | 1.5 | .2 | Setosa | |
| 4.4 | 2.9 | 1.4 | .2 | Setosa | |
| 4.9 | 3.1 | 1.5 | .1 | Setosa | |
| 5.4 | 3.7 | 1.5 | .2 | Setosa | |
| 4.8 | 3.4 | 1.6 | .2 | Setosa | |
| 4.8 | 3 | 1.4 | .1 | Setosa | |
| 4.3 | 3 | 1.1 | .1 | Setosa | |
| 5.8 | 4 | 1.2 | .2 | Setosa | |
| 5.7 | 4.4 | 1.5 | .4 | Setosa | |
| 5.4 | 3.9 | 1.3 | .4 | Setosa | |
| 5.1 | 3.5 | 1.4 | .3 | Setosa | |
| 5.7 | 3.8 | 1.7 | .3 | Setosa | |
| 5.1 | 3.8 | 1.5 | .3 | Setosa | |
| 5.4 | 3.4 | 1.7 | .2 | Setosa | |
| 5.1 | 3.7 | 1.5 | .4 | Setosa | |
| 4.6 | 3.6 | 1 | .2 | Setosa | |
| 5.1 | 3.3 | 1.7 | .5 | Setosa | |
| 4.8 | 3.4 | 1.9 | .2 | Setosa | |
| 5 | 3 | 1.6 | .2 | Setosa | |
| 5 | 3.4 | 1.6 | .4 | Setosa | |
| 5.2 | 3.5 | 1.5 | .2 | Setosa | |
| 5.2 | 3.4 | 1.4 | .2 | Setosa | |
| 4.7 | 3.2 | 1.6 | .2 | Setosa | |
| 4.8 | 3.1 | 1.6 | .2 | Setosa | |
| 5.4 | 3.4 | 1.5 | .4 | Setosa | |
| 5.2 | 4.1 | 1.5 | .1 | Setosa | |
| 5.5 | 4.2 | 1.4 | .2 | Setosa | |
| 4.9 | 3.1 | 1.5 | .2 | Setosa | |
| 5 | 3.2 | 1.2 | .2 | Setosa | |
| 5.5 | 3.5 | 1.3 | .2 | Setosa | |
| 4.9 | 3.6 | 1.4 | .1 | Setosa | |
| 4.4 | 3 | 1.3 | .2 | Setosa | |
| 5.1 | 3.4 | 1.5 | .2 | Setosa | |
| 5 | 3.5 | 1.3 | .3 | Setosa | |
| 4.5 | 2.3 | 1.3 | .3 | Setosa | |
| 4.4 | 3.2 | 1.3 | .2 | Setosa | |
| 5 | 3.5 | 1.6 | .6 | Setosa | |
| 5.1 | 3.8 | 1.9 | .4 | Setosa | |
| 4.8 | 3 | 1.4 | .3 | Setosa | |
| 5.1 | 3.8 | 1.6 | .2 | Setosa | |
| 4.6 | 3.2 | 1.4 | .2 | Setosa | |
| 5.3 | 3.7 | 1.5 | .2 | Setosa | |
| 5 | 3.3 | 1.4 | .2 | Setosa | |
| 7 | 3.2 | 4.7 | 1.4 | Versicolor | |
| 6.4 | 3.2 | 4.5 | 1.5 | Versicolor | |
| 6.9 | 3.1 | 4.9 | 1.5 | Versicolor | |
| 5.5 | 2.3 | 4 | 1.3 | Versicolor | |
| 6.5 | 2.8 | 4.6 | 1.5 | Versicolor | |
| 5.7 | 2.8 | 4.5 | 1.3 | Versicolor | |
| 6.3 | 3.3 | 4.7 | 1.6 | Versicolor | |
| 4.9 | 2.4 | 3.3 | 1 | Versicolor | |
| 6.6 | 2.9 | 4.6 | 1.3 | Versicolor | |
| 5.2 | 2.7 | 3.9 | 1.4 | Versicolor | |
| 5 | 2 | 3.5 | 1 | Versicolor | |
| 5.9 | 3 | 4.2 | 1.5 | Versicolor | |
| 6 | 2.2 | 4 | 1 | Versicolor | |
| 6.1 | 2.9 | 4.7 | 1.4 | Versicolor | |
| 5.6 | 2.9 | 3.6 | 1.3 | Versicolor | |
| 6.7 | 3.1 | 4.4 | 1.4 | Versicolor | |
| 5.6 | 3 | 4.5 | 1.5 | Versicolor | |
| 5.8 | 2.7 | 4.1 | 1 | Versicolor | |
| 6.2 | 2.2 | 4.5 | 1.5 | Versicolor | |
| 5.6 | 2.5 | 3.9 | 1.1 | Versicolor | |
| 5.9 | 3.2 | 4.8 | 1.8 | Versicolor | |
| 6.1 | 2.8 | 4 | 1.3 | Versicolor | |
| 6.3 | 2.5 | 4.9 | 1.5 | Versicolor | |
| 6.1 | 2.8 | 4.7 | 1.2 | Versicolor | |
| 6.4 | 2.9 | 4.3 | 1.3 | Versicolor | |
| 6.6 | 3 | 4.4 | 1.4 | Versicolor | |
| 6.8 | 2.8 | 4.8 | 1.4 | Versicolor | |
| 6.7 | 3 | 5 | 1.7 | Versicolor | |
| 6 | 2.9 | 4.5 | 1.5 | Versicolor | |
| 5.7 | 2.6 | 3.5 | 1 | Versicolor | |
| 5.5 | 2.4 | 3.8 | 1.1 | Versicolor | |
| 5.5 | 2.4 | 3.7 | 1 | Versicolor | |
| 5.8 | 2.7 | 3.9 | 1.2 | Versicolor | |
| 6 | 2.7 | 5.1 | 1.6 | Versicolor | |
| 5.4 | 3 | 4.5 | 1.5 | Versicolor | |
| 6 | 3.4 | 4.5 | 1.6 | Versicolor | |
| 6.7 | 3.1 | 4.7 | 1.5 | Versicolor | |
| 6.3 | 2.3 | 4.4 | 1.3 | Versicolor | |
| 5.6 | 3 | 4.1 | 1.3 | Versicolor | |
| 5.5 | 2.5 | 4 | 1.3 | Versicolor | |
| 5.5 | 2.6 | 4.4 | 1.2 | Versicolor | |
| 6.1 | 3 | 4.6 | 1.4 | Versicolor | |
| 5.8 | 2.6 | 4 | 1.2 | Versicolor | |
| 5 | 2.3 | 3.3 | 1 | Versicolor | |
| 5.6 | 2.7 | 4.2 | 1.3 | Versicolor | |
| 5.7 | 3 | 4.2 | 1.2 | Versicolor | |
| 5.7 | 2.9 | 4.2 | 1.3 | Versicolor | |
| 6.2 | 2.9 | 4.3 | 1.3 | Versicolor | |
| 5.1 | 2.5 | 3 | 1.1 | Versicolor | |
| 5.7 | 2.8 | 4.1 | 1.3 | Versicolor | |
| 6.3 | 3.3 | 6 | 2.5 | Virginica | |
| 5.8 | 2.7 | 5.1 | 1.9 | Virginica | |
| 7.1 | 3 | 5.9 | 2.1 | Virginica | |
| 6.3 | 2.9 | 5.6 | 1.8 | Virginica | |
| 6.5 | 3 | 5.8 | 2.2 | Virginica | |
| 7.6 | 3 | 6.6 | 2.1 | Virginica | |
| 4.9 | 2.5 | 4.5 | 1.7 | Virginica | |
| 7.3 | 2.9 | 6.3 | 1.8 | Virginica | |
| 6.7 | 2.5 | 5.8 | 1.8 | Virginica | |
| 7.2 | 3.6 | 6.1 | 2.5 | Virginica | |
| 6.5 | 3.2 | 5.1 | 2 | Virginica | |
| 6.4 | 2.7 | 5.3 | 1.9 | Virginica | |
| 6.8 | 3 | 5.5 | 2.1 | Virginica | |
| 5.7 | 2.5 | 5 | 2 | Virginica | |
| 5.8 | 2.8 | 5.1 | 2.4 | Virginica | |
| 6.4 | 3.2 | 5.3 | 2.3 | Virginica | |
| 6.5 | 3 | 5.5 | 1.8 | Virginica | |
| 7.7 | 3.8 | 6.7 | 2.2 | Virginica | |
| 7.7 | 2.6 | 6.9 | 2.3 | Virginica | |
| 6 | 2.2 | 5 | 1.5 | Virginica | |
| 6.9 | 3.2 | 5.7 | 2.3 | Virginica | |
| 5.6 | 2.8 | 4.9 | 2 | Virginica | |
| 7.7 | 2.8 | 6.7 | 2 | Virginica | |
| 6.3 | 2.7 | 4.9 | 1.8 | Virginica | |
| 6.7 | 3.3 | 5.7 | 2.1 | Virginica | |
| 7.2 | 3.2 | 6 | 1.8 | Virginica | |
| 6.2 | 2.8 | 4.8 | 1.8 | Virginica | |
| 6.1 | 3 | 4.9 | 1.8 | Virginica | |
| 6.4 | 2.8 | 5.6 | 2.1 | Virginica | |
| 7.2 | 3 | 5.8 | 1.6 | Virginica | |
| 7.4 | 2.8 | 6.1 | 1.9 | Virginica | |
| 7.9 | 3.8 | 6.4 | 2 | Virginica | |
| 6.4 | 2.8 | 5.6 | 2.2 | Virginica | |
| 6.3 | 2.8 | 5.1 | 1.5 | Virginica | |
| 6.1 | 2.6 | 5.6 | 1.4 | Virginica | |
| 7.7 | 3 | 6.1 | 2.3 | Virginica | |
| 6.3 | 3.4 | 5.6 | 2.4 | Virginica | |
| 6.4 | 3.1 | 5.5 | 1.8 | Virginica | |
| 6 | 3 | 4.8 | 1.8 | Virginica | |
| 6.9 | 3.1 | 5.4 | 2.1 | Virginica | |
| 6.7 | 3.1 | 5.6 | 2.4 | Virginica | |
| 6.9 | 3.1 | 5.1 | 2.3 | Virginica | |
| 5.8 | 2.7 | 5.1 | 1.9 | Virginica | |
| 6.8 | 3.2 | 5.9 | 2.3 | Virginica | |
| 6.7 | 3.3 | 5.7 | 2.5 | Virginica | |
| 6.7 | 3 | 5.2 | 2.3 | Virginica | |
| 6.3 | 2.5 | 5 | 1.9 | Virginica | |
| 6.5 | 3 | 5.2 | 2 | Virginica | |
| 6.2 | 3.4 | 5.4 | 2.3 | Virginica | |
| 5.9 | 3 | 5.1 | 1.8 | Virginica |
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| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score, log_loss | |
| import pandas as pd | |
| import lightgbm as lgb | |
| # Prepare training data | |
| df = pd.read_csv('data/iris.csv') | |
| flower_names = {'Setosa': 0, 'Versicolor': 1, 'Virginica': 2} | |
| X = df[['sepal.length', 'sepal.width', 'petal.length', 'petal.width']] | |
| y = df['variety'].map(flower_names) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| train_data = lgb.Dataset(X_train, label=y_train) | |
| def main(): | |
| # Train model | |
| params = { | |
| "objective": "multiclass", | |
| "num_class": 3, | |
| "learning_rate": 0.2, | |
| "metric": "multi_logloss", | |
| "feature_fraction": 0.8, | |
| "bagging_fraction": 0.9, | |
| "seed": 42, | |
| } | |
| model = lgb.train(params, train_data, valid_sets=[train_data]) | |
| # Evaluate model | |
| y_proba = model.predict(X_test) | |
| y_pred = y_proba.argmax(axis=1) | |
| loss = log_loss(y_test, y_proba) | |
| acc = accuracy_score(y_test, y_pred) | |
| if __name__ == "__main__": | |
| main() |
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