Another onw
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eb520584b1
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338efa332b
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@ -34,12 +34,17 @@ def preprocess_data(df_train, df_test):
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def predict():
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hyper_params = {
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'C': np.logspace(-3, 3, 7, 10, 20),
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'penalty': ['l1', 'l2', 'elasticnet'],
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'solver': ['liblinear', 'saga', 'lbfgs', 'newton-cg'],
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'max_iter': [50, 100, 1000, 2500, 5000],
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'class_weight': ['balanced', None],
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'tol': [1e-4, 1e-3, 1e-2, 1e-1, 1],
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"n_estimators": [50, 100, 200, 300, 400, 500, 600, 700],
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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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"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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"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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"subsample": [0.6, 0.7, 0.8, 0.9, 1.0],
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"max_features": ["auto", "sqrt", "log2"],
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}
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df_train, df_test = load_data()
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@ -47,11 +52,11 @@ def predict():
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X_train, y_train, X_test, y_test = preprocess_data(df_train, df_test)
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# Model
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from sklearn.linear_model import LogisticRegression
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.model_selection import GridSearchCV
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model = GridSearchCV(
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estimator=LogisticRegression(),
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estimator=GradientBoostingClassifier(),
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param_grid=hyper_params,
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cv=5,
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n_jobs=-1,
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@ -67,3 +72,17 @@ def predict():
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if __name__ == "__main__":
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predict()
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#### Logistic Regression
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# Best parameters: {'C': 20.0, 'class_weight': None, 'max_iter': 50, 'penalty': 'l2', 'solver': 'lbfgs', 'tol': 0.0001}
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# Accuracy: 0.8660287081339713
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# Hyperparams tuned:
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# hyper_params = {
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# 'C': np.logspace(-3, 3, 7, 10, 20),
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# 'penalty': ['l1', 'l2', 'elasticnet'],
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# 'solver': ['liblinear', 'saga', 'lbfgs', 'newton-cg'],
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# 'max_iter': [50, 100, 1000, 2500, 5000],
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# 'class_weight': ['balanced', None],
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# 'tol': [1e-4, 1e-3, 1e-2, 1e-1, 1],
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# }
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######################
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