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.
- Study on insect wing hinge control mechanics was conducted by researchers at California Institute of Technology.
- The study utilized a genetically encoded calcium indicator to image steering muscles activity in flies while tracking 3D wing motion.
- A Convolutional Neural Network (CNN) was trained to predict wing motion from steering muscle activity and wingbeat frequency.
- An encoder-decoder was employed to predict the role of individual sclerites on wing motion.
- Virtual experiments were carried out to assess the impact of modulating wing motion via steering muscle activity on aerodynamic forces.
- The study concludes that the insect wing hinge is a complex and evolutionarily significant skeletal structure.
Generating counterfactual explanations got a lot easier with CFNOW, but what are counterfactual explanations, and how can I use them?