如果设置为True,在之前的模型基础上预测并添加模型,否则,建立一个全新的森林; class_weight : dict, list of dicts, “balanced”, “balanced” 模式自动调整权重,每类的权重为 n_samples / (n_classes * np.bincount(y)),即类别数的倒数除以每类样本数的占比。 属性 estimators_ : list of DecisionTree...
acc =0.0temp =1conf_mat = [[0foriinrange(10)]forjinrange(10)] clf = RandomForestClassifier(n_estimators=20,max_features=None,class_weight="balanced_subsample") valid_mfcc=0.0; valid_fft=0.0; valid_kpca=0.0fortrain_index, test_indexinkf: X_train, X_test = X[train_index], X[test...
class_weight:每个类的权重,可以用字典的形式传入{class_label: weight}。如果选择了“balanced”,则输入的权重为n_samples / (n_classes * np.bincount(y))。 其余参数: criterion:分裂节点所用的标准,可选“gini”, “entropy”,默认“gini”。 max_depth:树的最大深度。如果为None,则将节点展开,直到所有...
class_weight:每个类的权重,可以用字典的形式传入{class_label: weight}。如果选择了“balanced”,则输入的权重为n_samples / (n_classes * np.bincount(y))。 ccp_alpha:将选择成本复杂度最大且小于ccp_alpha的子树。默认情况下,不执行修剪。 max_samples:如果bootstrap为True,则从X抽取以训练每个基本分类器的...
min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ...
class_weight:{“balanced”, “balanced_subsample”},字典或字典列表,默认=无 与{class_label: weight}形式的类关联的权重。如果没有给出,所有的类都应该有一个权重。对于multi-output 问题,可以按照与 y 的列相同的顺序提供字典列表。 请注意,对于多输出(包括多标签),应为其自己的字典中的每一列的每个类定...
Also the performance of the forest for different classes differs due to uneven class proportions. In this work, a novel voting mechanism is introduced: each leaf node has an individual weight. The final decision is not determined by majority voting but rather by a linear combination of ...
选自MachineLearningMastery 作者：Jason Brownlee 机器之心编译 参与：Nurhachu Null、路雪 判断长短期...
class_weight:每个类的权重,可以用字典的形式传入{class_label: weight}。如果选择了“balanced”,则输入的权重为n_samples / (n_classes * np.bincount(y))。 ccp_alpha:将选择成本复杂度最大且小于ccp_alpha的子树。默认情况下,不执行修剪。 max_samples:如果bootstrap为True,则从X抽取以训练每个基本分类器的...
class_weight="balanced", ) metric ="f1"losses = ["log","hinge","modified_huber","squared_hinge","perceptron"] penalties = ["l2","l1","elasticnet"] alphas =10.0** numpy.arange(-5,0) learning_rates = ["constant","optimal","invscaling"] ...