auto_class_weights参数可以自动根据样本的不平衡程度来调整类别的权重,从而实现样本平衡。这些参数的使用可以根据实际情况进行调整,以达到最佳的效果。 在实际应用中,根据多分类样本的不平衡程度和需求,可以选择合适的参数来平衡样本。通过平衡样本,可以提高模型的预测准确性和稳定性,从而更好地应对多分类问题。 CatBoost...
'auto_class_weights': , : 自动计算平衡各类别权重 'scale_pos_weight': , : 二分类中第1类的权重,默认值1(不可与class_weights、auto_class_weights同时设置) '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_...
auto_class_weights : string [default=None] Enables automatic class weights calculation. Possible values: - Balanced # weight = maxSummaryClassWeight / summaryClassWeight, statistics determined from train pool - SqrtBalanced # weight = sqrt(maxSummaryClassWeight / summaryClassWeight) class_names: list...
'Plain', 'bootstrap_type': 'Poisson', 'leaf_estimation_backtracking': 'Armijo', 'min_data_in_leaf': 16, 'one_hot_max_size': 12, 'has_time': True, 'random_seed': 42, 'task_type': 'GPU', 'devices': '0:1', 'subsample': 0.2479141447711061, 'auto_class_weights': 'Balanced'}...
Now CatBoost allows to specify use_weights for metrics when auto_class_weights parameter is set. Correctly handle NaN values in plot_predictions function. Fixed floating point precision drop releated bugs during Multiclass training with lots of objects in our case, bug was triggered while training ...
Added new auto_class_weights option in python-package, R-package and cli with possible values Balanced and SqrtBalanced. For Balanced every class is weighted maxSumWeightInClass / sumWeightInClass, where sumWeightInClass is sum of weights of all samples in this class. If no weights are present...
# 计算准确率 bind_cols(mpe_test %>% select(MPE)) %>% accuracy(MPE,.pred_class) ## # A tibble: 1 × 3 ## .metric .estimator .estimate ## <chr> <chr> <dbl> ## 1 accuracy binary 0.756 参考资料 参考 1、github.com/curso-r/tree 2、jianshu.com/p/bfa387b8a 更多R语言的知识请...
(w_i\)are calculated. This provides further motivation for research that elucidates how one might go about optimizing weights for ensembles involving CatBoost; since we have examples of research missing this information in two disparate fields – Finance, and here, Medicine. The evaluation metric ...
设计思路 输出结果 Pclass Sex Age SibSp Parch Survived 0 3 male 22.0 1 0 0 1 1 female 38.0 1 0 1 2 3 female 26.0 0 0 1 3 1 female 35.0 1 0 1 4 3 male 35.0 0 0 0 Pclass int64 Sex object Age float64 SibSp int64 Parch int64 Survived int64 dtype: object object_features_ID...
'boosting_type': 'Plain', 'bootstrap_type': 'Poisson', 'leaf_estimation_backtracking': 'Armijo', 'min_data_in_leaf': 7, 'one_hot_max_size': 11, 'has_time': True, 'random_seed': 42, 'task_type': 'GPU', 'devices': '0', 'subsample': 0.2769540222341547, 'auto_class_weights'...