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Cohen's Kappa is a measure of agreement between two raters, in this case, the machine learning model's predictions and the ground truth labels. It is a useful metric when evaluating the performance of a model that is making binary classifications, such as predicting whether an email is spam or not.

The Wilcoxon Signed Rank test is a non-parametric statistical test used to compare two related samples. It is used when the data is not normally distributed, or when the assumption of normality is violated.

<br>When working with small datasets, comparing the accuracy of a model can be challenging because the sample size may not be large enough to obtain reliable estimates of model performance. However, there are a few methods that can be used to compare the accuracy of models with small datasets:

Non-parametric statistical tests are a class of statistical tests that are <b>used to analyze data when the underlying distribution is unknown or when the data does not meet the assumptions of parametric tests</b> (i.e., tests that assume a specific distribution, such as normal distribution). Non-parametric tests do not rely on assumptions about the underlying distribution of the data and are therefore more robust to violations of these assumptions.

There are many classification algorithms in machine learning, some of the most commonly used ones are: