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6 votes

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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5 votes

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 ...
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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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0 votes

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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  • 1,259
0 votes

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 ...
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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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0 votes

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 ...
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0 votes

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

The bottom line is that the proportions of diseased subjects among smokers and non-smokers are 0.0037 and 0.0011, respectively, and they are highly significantly different. Because counts 16 and 20 ...
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1 vote

Dependent variable has no variance error in logit regression

The error message is clearly wrong: your data has a non-zero variance, though it is clearly imbalanced. What thus means for you? First, 115 positive samples are not much, if you can it might be wise ...
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