This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
This article introduces interpretable clustering, a field that aims to provide insights into the characteristics of clusters formed by clustering algorithms. It discusses the limitations of traditional clustering methods and highlights the benefits of interpretable clustering in understanding data patterns.
This article explains the concept and use of Friedman's H-statistic for finding interactions in machine learning models.
- The H-stat is a non-parametric method that works well with ordinal variables, and it's useful when the interaction is not linear.
- The H-stat compares the average rank of the response variable for each level of the predictor variable, considering all possible pairs of levels.
- The H-stat calculates the sum of these rank differences and normalizes it by the total number of observations and the number of levels in the predictor variable.
- The lower the H-stat, the stronger the interaction effect.
- The article provides a step-by-step process for calculating the H-stat, using an example with a hypothetical dataset about the effects of asbestos exposure on lung cancer for smokers and non-smokers.
- The author also discusses the assumptions of the H-stat and its limitations, such as the need for balanced data and the inability to detect interactions between more than two variables.
