num\_iterations或者num\_iteration或者num\_tree或者num\_trees或者num\_round或者num\_rounds或者num\_boost\_round一个整数,给出了boosting的迭代次数。默认为100。 对于Python/R包,该参数是被忽略的。对于Python,使用train()/cv()的输入参数num\_boost\_round来代替。 在内部,LightGBM对于multiclass问题设置了n...
num_iterations或者num_iteration或者num_tree或者num_trees或者num_round或者num_rounds或者num_boost_round一个整数,给出了boosting的迭代次数。默认为100。 对于Python/R包,该参数是被忽略的。对于Python,使用train()/cv()的输入参数num_boost_round来代替。 在内部,LightGBM对于...
num_boost_round的别名num_trees，即同一参数。
num\_iterations或者num\_iteration或者num\_tree或者num\_trees或者num\_round或者num\_rounds或者num\_boost\_round一个整数,给出了boosting的迭代次数。默认为100。 对于Python/R包,该参数是被忽略的。对于Python,使用train()/cv()的输入参数num\_boost\_round来代替。 在内部,LightGBM对于multiclass问题设置了n...
lgbm gbdt (gradient boosted decision trees) dart gradient boosting lgbm goss (Gradient-based One-Side Sampling) 正则化(regularization) lambda_l1: mun_leaves: subsample feature_fraction max_depth max_bin training parameters num_iterations early_stopping_rounds lightgbm categorical_feature lightgbm is_unba...
num_iterations或者num_iteration或者num_tree或者num_trees或者num_round或者num_rounds或者num_boost_round一个整数,给出了boosting的迭代次数。默认为100。 对于Python/R包,该参数是被忽略的。对于Python,使用train()/cv()的输入参数num_boost_round来代替。
‘dart’:基于Dropout的多重加性回归树(Dropouts meet Multiple Additive Regression Trees,DART)。 ‘goss’:基于梯度的单边采样(Gradient-based One-Side Sampling,GOSS)。 默认是GDBT,即经典的梯度提升算法。DART 算法是在 2015 年的一篇论文中提出的,标题为 “DART:Dropouts meet Multiple Additive Regression Trees...
fromhyperoptimportfmin,tpe,hp,partial# 自定义hyperopt的参数空间space={"max_depth":hp.randint("max_depth",15),"num_trees":hp.randint("num_trees",300),'learning_rate':hp.uniform('learning_rate',1e-3,5e-1),"bagging_fraction":hp.randint("bagging_fraction",5),"num_leaves":hp.randint(...
# lightgbm_config.json{"objective":"binary","task":"train","boosting":"gbdt","num_iterations":500,"learning_rate":0.1,"max_depth":-1,"num_leaves":64,"tree_learner":"serial","num_threads":0,"device_type":"cpu","seed":0,"min_data_in_leaf":100,"min_sum_hessian_in_leaf":0.001...
采用小的`max_bin`、小的`num_leaves`、`min_data_in_leaf`和`min_sum_hessian_in_leaf`，结合Bagging和特征子采样，使用正则化参数`lambda_l1`、`lambda_l2`和`min_gain_to_split`，限制`max_depth`，尝试`extra_trees`或增加`path_smooth`，可以有效防止过拟合。综上所述，通过合理调整...