10 lines
272 B
Python
10 lines
272 B
Python
import xgboost as xgb
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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clf = xgb.XGBClassifier()
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clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
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eval_set=[(X_test, y_test)])
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