# The actual number of trees build will depend on early stopping and 5000 define only the absolute maximum clf = lgb.LGBMClassifier(max_depth=-1, random_state=314, silent=False, metric='None', n_jobs=-1, n_estimators=5000) gs = RandomizedSearchCV( estimator = clf, param_distributions =...
cvparams[lgb_param] = sklearn_params[sklearn_param] cat_vars='auto' lgbc=lgb.LGBMClassifier(**params) get_lgb_params(lgbc.get_params(),cvparams,tran_table) modelfit(lgbc,x,y,cat_vars,cvparams,feature_names,useTrainCV=True,cv_folds=5,early_stopping_rounds=30, metric='f1_macpro') Hi...
train_test_split param_grid = { 'n_estimators' : [10, 100], 'boosting_type': ['gbdt'], 'num_leaves': [8, 10], 'subsample': [0.8, 0.95], 'is_unbalance': [True, False], 'min_split_gain' :[0.01, 0.02, 0.05] } lgb = LGBMClassifier...
import lightgbm as lgb import numpy as np feature_names=['one','two','three','four','five'] train_data = np.random.random((100000,5)) train_labels = np.round(np.random.random(100000)).astype(int) test_data = np.random.random((20000,5)) test_labels = np.round(np.random.random...