Kolmogorov-Smirnov Test
Kolmogorov-Smirnov Test is a statistical test used to determine whether two datasets are significantly different from each other. It is a non-parametric test that compares the cumulative distributions of two datasets. The test calculates the maximum difference between the two cumulative distributions, which is known as the test statistic. The test statistic is compared to a critical value from a table or calculated using a formula, and if it exceeds the critical value, the null hypothesis is rejected, indicating that the two datasets are significantly different. The Kolmogorov-Smirnov Test is commonly used in data science and machine learning to compare the distributions of two datasets, such as a model's predicted values and actual values, or two different models' predicted values.
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