Gini(D)越小越好 使用属性α划分后的基尼指数为: 在候选属性集合A中,选择那个使得划分后基尼指数最小的属性作为最优划分属性 <决策树常用算法> ID3 (使用information gain划分属性,处理离散型特征,离散型response) 分类 C4.5 (使用gain ratio划分属性,处理离散型、连续型特征,离散型response)分类 CART(用gini inde...
对于决策树C&RT算法,通常来说,上面介绍的各种impurity functions中,Gini index更适合求解classification问题,而regression error更适合求解regression问题。 Decision Tree Heuristics in C&RT 了C&RT算法的基本流程: 可以看到C&RT算法在处理binary classification和regression问题时非常简单实用,而且,处理muticlass classification...
Gini index的优点是将所有的class在数据集中的分布状况和所占比例全都考虑了,这样让decision stump的选择更加准确。 对于决策树C&RT算法,通常来说,上面介绍的各种impurity functions中,Gini index更适合求解classification问题,而regression error更适合求解regression问题。 C&RT算法迭代终止条件有两种情况,第一种情况是当前...
这样,最终得到包含不同叶子的几种模型,将这几个使用regularized decision tree的error function来进行选择,确定包含几片叶子的模型误差最小,就选择该模型。另外,参数λ λ 可以通过validation来确定最佳值。 我们一直讨论决策树上的叶子(features)都是numerical features,而实际应用中,决策树的特征值可能不是数字量,而是...
上面这个式子只考虑纯度最大的那个分支,更好的做法是将所有分支的纯度都考虑并计算在内,用基尼指数(Gini index)表示:$$1-\sumK_{k=1}(\frac{\sumN_{n=1}[y_n=k]}{N})^2$$ Gini index的优点是将所有的class在数据集中的分布状况和所占比例全都考虑了,这样让decision stump的选择更加准确。 对于决策...
Gini index的优点是将所有的class在数据集中的分布状况和所占比例全都考虑了,这样让decision stump的选择更加准确。 对于决策树C&RT算法,通常来说,上面介绍的各种impurity functions中,Gini index更适合求解classification问题,而regression error更适合求解regression问题。
gini index: A measure of inequality between the distributions of label characteristics. Splitting on a chosen Attribute results in a reduction in the average gini index of the resulting subsets. accuracy: An Attribute is selected for splitting, which maximizes the accuracy of the whole tree. ...
The Gini index or impurity is a measure of a criterion to lessen the probability of misclassification. Entropy or information gain provides with the amount of disorder in a set which means that when entropy is zero, all the points of the target classes are the same. Several separate tree ...
On the condition of feature A, the Gini index of set D is defined as: (6)GiniDA=D1DGiniD1+D2DGiniD2 ➢ In the second step, the algorithm generates decision tree from root node, until all training datasets being correctly classified. ➢ Last but not the least, the final decision ...
Regression Trees: In this type of algorithm, the decision or result is continuous. It has got a single numerical output with more inputs or predictors. In the Decision tree, the typical challenge is to identify the attribute at each node. The process is called attribute selection and has som...