# Tag Info

### class weighted classification

Against @gunes, I defend that you can use whatever metric you want. Yes accuracy may give you unexpected results in a imbalanced problem, but the choice of metric is based on the needs of your problem....
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### class weighted classification

If you want to include accuracy as a performance metric, balanced accuracy is a better choice than accuracy because of the imbalance in class distributions here. I would also recommend reading these ...
• 51.1k
1 vote
Accepted

### What metrics work well in unbalanced assemblies?

The choice of metric depends on the needs of the application, not the problems with the methods/tools. Accuracy is not a very bad metric; the main problem is that practitioners fail to use the ...
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### Do we need to split the data for Unsupervised Anomaly Detection?

The splitting of datasets is used to give an estimate of generalized performance, and is used for predictive models - models that are designed to take new datapoints and output new predictions for ...
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### References: t-test and Chi-squared test can be conducted with unequal sample sizes

Many intermediate-level applied statistical texts have the warnings mentioned in my comment. (One example, among many, is the text by Ott & Longnecker.) Use Welch, not pooled t test. Here is an ...
• 49.7k
1 vote

### How to correct Chi-square's p-value when working with very unbalanced contingency tables?

In addition to the other excellent answers: To get more precise inference, you can model via logistic regression. That can always be done with a $2\times 2$ contingency table, and then use likelihood ...
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### How to correct Chi-square's p-value when working with very unbalanced contingency tables?

Imbalance alone is not an issue for a chi-squared test, although a small absolute number of counts can be - applying a chi-squared test to a 100:1 imbalanced dataset will work fine if you have a ...
• 5,733