参数:params:一个字典,给出了新的参数 .rollback_one_iter(): 将Booster回滚一个迭代步 .save_model(filename,num_iteration=-1): 保存Booster对象到文件中。 参数: filename: 一个字符串,给出了保存的文件的文件名 num_iteration: 一个整数,指定需要保存的是第几轮训练的结果。如果小于0,则最佳迭代步的结...
/ (1. + np.exp(-preds)) return 'accuracy', np.mean(labels == (preds > 0.5)), True # 训练+预测 model = lgb.train(params,train_set=lgb_train,num_boost_round=200,valid_sets=lgb_eval ,valid_names='nonsense',feval=accuracy1) y_ = model.predict(x_test) y_...
'eval_set': [(train,df_train[label_col]), (val,df_val[label_col])], 'verbose':20, 'callbacks': [lgb.reset_parameter(learning_rate=learning_rate_power)]} lgb_clf.fit(train,df_train[label_col],**fit_params) print('Training accuracy: ',accuracy_score(df_train[label_col],lgb_clf...
eval_set = [(df_cv_train[self.feature_list], df_cv_train[self.label]), (df_cv_valid[self.feature_list], df_cv_valid[self.label]) ] model.fit(df_cv_train[self.feature_list], df_cv_train[self.label], eval_set=eval_set, eval_metric=lambda ytrue, yprob: [eval_auc(ytrue, y...
is_unbalance或者unbalanced_set:一个布尔值,指示训练数据是否均衡的。默认为True。它用于二分类任务。 max_position:一个整数,指示将在这个NDCG位置优化。默认为20。它用于lambdarank任务。 label_gain:一个浮点数序列,给出了每个标签的增益。默认值为0,1,3,7,15,….它用于lambdarank任务。
# 3. trainbst = lgb.train(params=config, train_set=train_data, valid_sets=[val_data]) # 4. predictlgb.predict(val_data) # lightgbm_config.json{"objective":"binary","task":"train","boosting":"gbdt","num_iterations":500,"learning_rate":0.1,"max_depth":-1,"num_leaves":64,"tree...
dtrain=lgb.Dataset(Xtrain,label=ytrain)dtest=lgb.Dataset(Xtest,label=ytest)params={'num_leaves':31,# 最大的叶子节点数'max_depth':-1,# 树的最大深度,-1表示没有深度限制'tree_learner':'serial','application':'multiclass',# 目标是做分类还是回归任务'num_class':10,'learning_rate':0.1,...
params = { 'learning_rate': 0.1, 'lambda_l1': 0.1, 'lambda_l2': 0.2, 'max_depth': 4, 'objective': 'multiclass', # 目标函数 'num_class': 3, } # 模型训练 gbm = lgb.train(params, train_data, valid_sets=[validation_data]) ...
st.set_option('deprecation.showPyplotGlobalUse', False) st.sidebar.subheader("请选择模型参数:sunglasses:") # 加载数据 breast_cancer = load_breast_cancer() data = breast_cancer.data target = breast_cancer.target # 划分训练数据和测试数据 ...
(params,train_set=dtrain,num_boost_round=10,valid_sets=None,valid_names=None,fobj=None,feval=None,init_model=None,feature_name='auto',categorical_feature='auto',early_stopping_rounds=None,evals_result=None,verbose_eval=True,keep_training_booster=False,callbacks=None)t1=time.time()print('cpu...