'boosting_type': , : 提升类型,取值Ordered(catboost特有的排序提升,在小数据集上效果可能更好,但是运行速度较慢)、Plain(经典提升) 'feature_weights': , : 特征权重,在子树分裂时计算各特征的信息增益✖️该特征权重,选取最大结果对应特征分裂;设置方式:1、feature_weights = [0.1, 1, 3];2、feature_...
1、则左节点A为height>170,右节点B为heigh<170; 2、此时我们有两个叶子节点A和B,正常按照xgb的做法,A继续在所有feature里research找分裂增益最大的叶子节点,B也是在所有feature里面找分裂增益最大的,比如A找到weight>65为分裂节点继续分裂,而B找到age>20为分裂节点继续分裂,这是传统的level-wise;而按照lgb的做法...
model_size_reg=None,rsm=None,loss_function=None,border_count=None,feature_border_type=None,per_float_feature_quantization=None,input_borders=None,output_borders=None,fold_permutation_block=None,od_pval=None,od_wait=None,od_type=None,nan_mode=None,counter_calc_method=None,leaf_estimation_iteration...
如果有遗漏,具体可以参阅CatBoost python-reference_parameters-list区分具体的机器学习任务有:CatBoostClassifierCatBoostClassifier class CatBoostClassifier(iterations=None, learning_rate=None, depth=None, l2_leaf_reg=None, model_size_reg=None, rsm=None, loss_function=None, border_count=None, feature_border_...
feature_weights=None, penalties_coefficient=None, first_feature_use_penalties=None, per_object_feature_penalties=None, model_shrink_rate=None, model_shrink_mode=None, langevin=None, diffusion_temperature=None, posterior_sampling=None, boost_from_average=None, text_features=None, tokenizers=None, dic...
像CatBoostClassifier,除了loss_function, classes_count, class_names和class_weights def __init__( self, iterations=None, learning_rate=None, depth=None, l2_leaf_reg=None, model_size_reg=None, rsm=None, loss_function='RMSE', border_count=None, feature_border_type=None, per_float_feature_quan...
feature_weights=None, penalties_coefficient=None, first_feature_use_penalties=None, per_object_feature_penalties=None, model_shrink_rate=None, model_shrink_mode=None, langevin=None, diffusion_temperature=None, posterior_sampling=None, boost_from_average=None): ...
feature_weights=None, penalties_coefficient=None, first_feature_use_penalties=None, per_object_feature_penalties=None, model_shrink_rate=None, model_shrink_mode=None, langevin=None, diffusion_temperature=None, posterior_sampling=None, boost_from_average=None): ...
feature_weights=None, 689. penalties_coefficient=None, 690. first_feature_use_penalties=None, 691. per_object_feature_penalties=None, 692. model_shrink_rate=None, 693. model_shrink_mode=None, 694. langevin=None, 695. diffusion_temperature=None, 696. posterior_sampling=None, 697. boost_from...
baseline=None,feature_names=None,thread_count=-1) 代码语言:javascript 复制 from catboostimportCatBoostClassifier,Pool train_data=Pool(data=[[1,4,5,6],[4,5,6,7],[30,40,50,60]],label=[1,1,-1],weight=[0.1,0.2,0.3])train_data ...