boosting

How to pass multiple hyperparameters to LightGBM after optimization? Question: I have used another optimization algorithm that returns me best params for Light GBM. hyper_optimized_clf_classifier = Util.hp_opt(lgb.LGBMClassifier(silent=True, random_state=1), X, y, score, verbose=True, n_estimators =(hp.quniform,50,500,50), learning_rate =(hp.qloguniform, np.log(0.05), np.log(0.4),0.001), min_child_weight =(hp.qloguniform,np.log(3),np.log(200),1), reg_lambda = (hp.qloguniform, np.log(2), np.log(100),1), num_leaves = (hp.qloguniform, np.log(5),np.log(64),1), subsample = (hp.quniform, 0.5, 1, 0.05), …