ccp_alpha:non-negative float, default=0.0,用于最小成本复杂性修剪的复杂性参数。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。 monotonic_cst:array-like of int of shape (n_features), default=None,表示对每个特征执行的单调性约束。 Neural network models neural_network.Bernoulli...
max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort='deprecated', ccp_alpha=0.0) ...
max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort='deprecated', ccp_alpha=0.0) ...
如果需要具体区分哪些是属性,哪些是函数,可以通过ipython解释器中的自动补全功能。 大致浏览上述结果,属性主要是决策树初始化时的参数,例如ccp_alpha:剪枝系数,class_weight:类的权重,criterion:分裂准则等;还有就是决策树实现的主要函数,例如fit:模型训练,predict:模型预测等等。 本文的重点是探究决策树中是如何保存训练...
ccp_alphas, impurities = path.ccp_alphas,path.impurities trees = [] foralphainccp_alphas: tree3 = DecisionTreeClassifier(criterion='gini',ccp_alpha=alpha) tree3.fit(x_train,y_train) trees.append(tree3) trees = trees[:-1] ccp_alphas = ccp_alphas[:-1] ...
class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)...
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。 可选函数: classes_:类标签(单输出问题)或类标签数组的列表(多输出问题)。 feature_importances_:特征重要度。 max_features_:max_features的推断值。 n_classes_:类数(用于单输出问题),或包含每个输出的类数的列表(用于多输出...
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, ...
>>>forest.get_params(){'bootstrap':True,'ccp_alpha':0.0,'criterion':'mse','max_depth':None,'max_features':'auto','max_leaf_nodes':None,'max_samples':None,'min_impurity_decrease':0.0,'min_impurity_split':None,'min_samples_leaf':1,'min_samples_split':2,'min_weight_fraction_leaf...
ccp_alpha:非负浮点数,默认=0.0 用于最小Cost-Complexity 修剪的复杂度参数。将选择具有最大成本复杂度且小于ccp_alpha的子树。默认情况下,不进行剪枝。有关详细信息,请参阅最小 Cost-Complexity 修剪。 max_samples:int 或浮点数,默认=无 如果bootstrap 为 True,则从 X 抽取的样本数以训练每个基本估计器。