百度试题 题目sklearn.tree.DecisionTreeClassifier的参数max_depth表示决策树最大深度,模型样本数量多,特征也多时,推荐限制这个最大深度。() A.正确B.错误相关知识点: 试题来源: 解析 A 反馈 收藏
sklearn.tree.DecisionTreeClassifier的参数max_depth表示决策树最大深度,模型样本数量多,特征也多时,推荐限制这个最大深度。()
def base_of_decision_tree(max_depth):wine = datasets.load_wine# 仅选前两个特征X = wine.data[:,:2]y = wine.targetX_train,X_test,y_train,y_test = train_test_split(X, y)clf = DecisionTreeClassifier(max_depth=max_depth)clf.fit(X_train,y_train)#定义图像中分区的颜色和散点的颜色,...
关于决策树DecisionTreeClassifier的参数max_depth设置,下列正确的是( )。A.max_depth越大,模型越简单B.max_depth越小,模型越复杂C.max_depth越小,模型泛化能力越强。D.max_depth越大,模型泛化能力越差。的答案是什么.用刷刷题APP,拍照搜索答疑.刷刷题(shuashuati.co
(estimator, X, y);如果是None,则使用estimator的误差估计函数。...2、接下来要调试的参数是min_child_weight以及max_depth:注意:每次调完一个参数,要把 other_params对应的参数更新为最优值...参数的最佳取值:{'min_child_weight': 5, 'max_depth': 4} 最佳模型得分:0.94369522247392 由输出结果可知参...
We prove an exponential lower bound on the size of any fixed degree algebraic decision tree for solving MAX, the problem of finding the maximum of n real numbers. This complements the n ?1 lower bound of [Rabin (1972)] on the depth of algebraic decision trees for this problem. The ...
(0), splits(1)) val numClasses = 2 val categoricalFeaturesInfo = Map[Int, Int](0 -> 20, 1 -> 30) val impurity = "gini" val maxDepth = 12 val maxBins = 32 val model = DecisionTree.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, impurity, maxDepth, maxBins...
I agree. My intuition is that if max_depth / max_leaf_nodes are high enough, the tree still has a chance to recover from a bad feature subset draw at a given split nodes while if we do it a tree-based level, the whole tree is wasted. Member amueller commented Jan 29, 2020 @Dr...
(3) God’s love is past finding out. “O the depth of the riches both of the wisdom and knowledge of God! How unsearchable are his judgments, and his ways past finding out! For who hath known the mind of the Lord? Or who hath been his counsellor? Or who hath first given to him...
clf = TreeBoosting(max_deph=client_flex_model['clients_params']['max_depth']) @@ -257,10 +272,15 @@ def train_single_tree_at_client(client_flex_model, client_data, *args, **kwargs) client_flex_model['clients_params']['estimators'].append(clf) # Update the base_preds after buil...