交叉验证的Scikits混淆矩阵 [英] scikits confusion matrix with cross validation

问题描述

我正在使用scikits接口通过交叉验证(stratifiedKfold)训练svm分类器.对于每个(k个)测试集，我得到一个分类结果.我想要一个包含所有结果的混淆矩阵.Scikits具有混淆矩阵界面:sklearn.metrics.confusion_matrix(y_true，y_pred)我的问题是如何累积y_true和y_pred值.它们是数组(numpy).是否应该根据我的k折参数定义数组的大小?对于每个结果，我应该将y_true和y-pred添加到数组中????

I am training a svm classifier with cross validation (stratifiedKfold) using the scikits interfaces. For each test set (of k), I get a classification result. I want to have a confusion matrix with all the results. Scikits has a confusion matrix interface: sklearn.metrics.confusion_matrix(y_true, y_pred) My question is how should I accumulate the y_true and y_pred values. They are arrays (numpy). Should I define the size of the arrays based on my k-fold parameter? And for each result I should add the y_true and y-pred to the array ????

推荐答案

您可以使用汇总混淆矩阵，也可以为每个CV分区计算一个矩阵，然后为每个CV分区计算平均值和标准偏差(或标准误差).矩阵作为变异性的度量.

You can either use an aggregate confusion matrix or compute one for each CV partition and compute the mean and the standard deviation (or standard error) for each component in the matrix as a measure of the variability.

对于分类报告，需要修改代码以接受二维输入，以便通过每个CV分区的预测，然后计算每个类别的平均得分和标准差.

For the classification report, the code would need to be modified to accept 2 dimensional inputs so as to pass the predictions for each CV partitions and then compute the mean scores and std deviation for each class.

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