LightGBM parameters for classification problem.

lightgbm_clf_params.py

# Production parameters
lgbm_params = {
    'boosting_type': 'goss',    # Gradient-based One-Side Sampling (goss)
    'learning_rate': 0.05,      # Boosting learning rate, default = 0.1
    'n_estimators': 10000,      # Number of boosted trees to fit, default = 100
    'objective': 'binary',      # the learning task and the corresponding learning objective, default = None
    'class_weight': 'balanced', # Weights associated with classes, The ‘balanced’ mode*
    'reg_alpha': 0.1,           # L1 regularization term on weights (L1 penalty), default = 0.0
    'reg_lambda': 0.1,          # L2 regularization term on weights (L2 penalty), default = 0.0
    'random_state': 67,
    'n_jobs': -1,
}
# *uses the values of y to automatically adjust weights inversely proportional to class frequencies in the
# input data as n_samples / (n_classes * np.bincount(y))

# Search parameters
srch_params = {
    'boosting_type': 'gbdt',    # Gradient Boosting Decision Tree
    'num_leaves': 100,          # Maximum tree leaves for base learners
    'max_depth': 13,            # Maximum tree depth for base learners
    'learning_rate': 0.3,       # Boosting learning rate, default = 0.1
    'n_estimators': 10000,      # Number of boosted trees to fit, default = 100
    'objective': 'binary',      # the learning task and the corresponding learning objective, default = None
    'is_unbalance': True,       # Weights associated with classes, same as ['class_weight': 'balanced']
    'subsample': .8,            # Subsample ratio of the training instance
    'subsample_freq': 10,       # frequency for bagging, bagging at every k-iteration.
    'reg_alpha': 0.1,           # L1 regularization term on weights (L1 penalty), default = 0.0
    'reg_lambda': 0.1,          # L2 regularization term on weights (L2 penalty), default = 0.0
    'random_state': 67,
    'n_jobs': -1,
}