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import joblib |
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import numpy as np |
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from sklearn.datasets import fetch_california_housing |
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from sklearn.ensemble import AdaBoostRegressor |
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from sklearn.model_selection import train_test_split |
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random_seed = 0 |
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np.random.seed(random_seed) |
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dataset = fetch_california_housing() |
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X, y = dataset.data, dataset.target |
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X_train, _, y_train, _ = train_test_split(X, y, test_size=0.25, random_state=random_seed) |
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model = AdaBoostRegressor(n_estimators=100, random_state=random_seed) |
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model.fit(X_train, y_train) |
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joblib.dump(model, 'adaboost_regressor.joblib') |
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