classification

Loss function for class imbalanced binary classifier in Tensor flow

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You can add class weights to the loss function, by multiplying logits. Regular cross entropy loss is this: loss(x, class) = -log(exp(x[class]) / (\sum_j exp(x[j]))) = -x[class] + log(\sum_j exp(x[j])) in weighted case: loss(x, class) = weights[class] * -x[class] + log(\sum_j exp(weights[class] * x[j])) So by multiplying logits, you are re-scaling predictions of each class … Read more

How to interpret weka classification?

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Below is some sample output for a naive Bayes classifier, using 10-fold cross-validation. There’s a lot of information there, and what you should focus on depends on your application. I’ll explain some of the results below, to get you started. === Stratified cross-validation === === Summary === Correctly Classified Instances 71 71 % Incorrectly Classified … Read more