lgb_params = {
'n_estimators': 10000,
'learning_rate': 0.05,
'num_leaves': 20,
'max_depth': 8,
'colsample_bytree': 'auto',
'subsample': 1,
# 'reg_alpha': 0.04,
# 'reg_lambda': 0.075,
'reg_lambda': 100,
'min_split_gain': 0.5,
# 'min_child_weight': 10,
'min_child_samples': 50,
'random_state': 71,
# 'boosting_type': 'dart',
'silent': -1,
'verbose': -1,
'n_jobs': -1,
'metric': 'auc',
# 'is_unbalance': True,
}
fit_params = {
'eval_metric': 'auc',
'early_stopping_rounds': 250,
'verbose': 100,
}
if __name__ == '__main__':
run(NAME, feats, lgb_params, fit_params, fill=-9999)
# 'prev_null_count', 'prev_amount_to_main'
]
lgb_params = {
'n_estimators': 10000,
'learning_rate': 0.03,
'num_leaves': 20,
'max_depth': 8,
'colsample_bytree': 0.3,
'subsample': 0.8,
'reg_alpha': 0.04,
'reg_lambda': 0.075,
'min_split_gain': 0.02,
'min_child_weight': 10,
'random_state': 71,
# 'boosting_type': 'dart',
'silent': -1,
'verbose': -1,
'n_jobs': -1,
'metric': 'auc',
}
fit_params = {
'eval_metric': 'auc',
'early_stopping_rounds': 300,
'verbose': 100,
}
if __name__ == '__main__':
run(NAME, feats, lgb_params, fit_params)