信息(information)、熵(entropy)、信息增益(information gain)、基尼指数(Gini index)的概念,程序员大本营,技术文章内容聚合第一站。
区别在于: 当我们用 Entropy 的时候,与之对应的是 Information Gain,简称 IG(因为 entropy 本身有信息熵的意思) 当我们用 Gini Index 的时候,与之对应的就不能叫 IG了,而是叫 Gain。 是不是很简单?可能是由于知识点过细,或者很多时候我们根本不区分 Gian 和 Infomation Gain。当你百度谷歌的时候发现,大家都在...
1、Gini Index与AUC的关系:特定情况下Gini=2AUC-1 gini:measure how often a randomly chosen element from the set would be incorrectly labeled。 https://blog.csdn.net/u012735708/article/details/86002858 2、Gini Index与KS的关系: https://blog.csdn.net/buptdavid/article/details/84308900 "单一"变量...
决策树特征选择准则较常采用的三种指标是:()?A.信息增益(Information Gain)B.基尼指数(Gini Index )C.信息增益率(Informatio
基尼不纯度(Gini impurity)是决策树中用来衡量数据集分裂的一个指标。它源自经济学中的基尼系数,用来衡量财富或收入的分配公平性,但在决策树算法中,基尼不纯度是用来衡量一个数据集的混乱程度的。一个数据集的基尼不纯度反映了在这个数据集中随机选取两个项,这两个项属于不同类别的概率。基尼不纯度越大,数据集的...
Evolutionary feature construction using information gain and gini index. Muharram M.A,Smith G.D. Genetic Programming . 2004Muharram, M.A., Smith, G.D.: Evolutionary Feature Construction Using Informa- tion Gain and Gini Index. In: Keijzer, M., O'Reilly, U.-M., Lucas, S., Costa, E....
Information gain calculates the reduction in entropy or surprise from transforming a dataset in some way. It is commonly used in the construction of decision trees from a training dataset, by evaluating the information gain for each variable, and selecting the variable that maximizes the information...
Compared with the K-nearest neighbor algorithm,the error rate of the weighted K-nearest neighbor algorithm based on the information gain and Gini integrity comprehensive index is lower than the traditional K-nearest neighbor algorithm and the information-gain-weighted K-nearest neighbor algorithm. The...
Gini: Entropy: And that I should select the parameters that minimises the impurity. However in the specific DecisionTreeClassifier I can choose the criterion: Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. DecisionTreeClassifier What I don't ...
Information gain ratio: the ratio of the information gain g(D, A) to the entropy HA(D) of the training data set D with respect to the value of the feature A. Gini index: Gini (D) represents the uncertainty of set D. The larger the Gini index, the greater the uncertainty of the ...