classCatBoostClassifier(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_type=None,per_float_feature_quantization=None,input_borders=None,output_borders=None,fold_permutation_block=None,od_pval=None,od_wa...
通过调整class_weights参数,可以有效地平衡多分类样本,提高模型的性能。 另一个平衡多分类样本的参数是scale_pos_weight。这个参数主要用于解决二分类问题中正负样本不平衡的情况,但在CatBoost中也可以用于多分类问题。scale_pos_weight参数可以通过调整正样本的权重来平衡样本。在多分类问题中,可以将数量较少的类别作为...
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_type=None, per_float_feature_quantization=None, input_borders=None, output_borders=None, fold_permutation_block=None,...
'class_weights': , : y标签类别权重、用于类别不均衡处理,默认各类权重均为1 '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...
class_weight:设置数据集中不同类别样本的权重,默认为None,也就是所有类别的样本权重均为1,数据类型为字典或者字典列表(多类别) balanced:根据数据集中的类别的占比来按照比例进行权重设置n_samples/(n_classes*np.bincount(y)) balanced_subsamples:类似balanced,不过权重是根据自助采样后的样本来计算 ...
class_weight:设置数据集中不同类别样本的权重,默认为None,也就是所有类别的样本权重均为1,数据类型为字典或者字典列表(多类别) balanced:根据数据集中的类别的占比来按照比例进行权重设置n_samples/(n_classes*np.bincount(y)) balanced_subsamples:类似balanced,不过权重是根据自助采样后的样本来计算 ...
_class__"] for key, value in iteritems(locals().copy()): if key not in not_params and value is not None: params[key] = value super(CatBoostClassifier, self).__init__(params) def fit(self, X, y=None, cat_features=None, text_features=None, embedding_features=None, sample_weight...
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...
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, ...
model = CatBoostClassifier(iterations=2, random_seed=0, loss_function="MultiClass") data = map_cat_features(pool.get_features(), pool.get_cat_feature_indices()) model.fit(data, pool.get_label(), pool.get_cat_feature_indices(), sample_weight=np.arange(1, pool.num_row()+1), baseline...