Update randomized searchCV
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338efa332b
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87db4a6457
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@ -38,8 +38,6 @@ def predict():
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"learning_rate": [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5],
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"learning_rate": [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5],
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"max_depth": [1, 3, 4, 5, 6, 7, 8, 9, 10],
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"max_depth": [1, 3, 4, 5, 6, 7, 8, 9, 10],
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"min_samples_split": [2, 5, 10, 15, 100],
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"min_samples_split": [2, 5, 10, 15, 100],
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"ccp_alpha": [0.0, 0.001, 0.005, 0.01, 0.05],
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"loss": ["deviance", "exponential"],
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"tol": [1e-4, 1e-3, 1e-2, 1e-1, 1e-0],
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"tol": [1e-4, 1e-3, 1e-2, 1e-1, 1e-0],
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"validation_fraction": [0.1, 0.2, 0.3, 0.4, 0.5],
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"validation_fraction": [0.1, 0.2, 0.3, 0.4, 0.5],
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"min_samples_leaf": [1, 2, 5, 10],
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"min_samples_leaf": [1, 2, 5, 10],
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@ -53,14 +51,16 @@ def predict():
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# Model
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# Model
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.model_selection import GridSearchCV
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from sklearn.model_selection import RandomizedSearchCV
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model = GridSearchCV(
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model = RandomizedSearchCV(
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estimator=GradientBoostingClassifier(),
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GradientBoostingClassifier(),
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param_grid=hyper_params,
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hyper_params,
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cv=5,
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n_iter=100000,
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scoring="roc_auc",
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n_jobs=-1,
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n_jobs=-1,
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verbose=1
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cv=5,
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verbose=1,
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)
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)
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model.fit(X_train, y_train)
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model.fit(X_train, y_train)
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