This article explores the use of Isolation Forest for anomaly detection and how SHAP (KernelSHAP and TreeSHAP) can be applied to explain the anomalies detected, providing insights into which features contribute to anomaly scores.
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.
- 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.
