Category: Statistics
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It’s the interactions
What makes a ML model a black-box? It is the interactions. Without any interactions, the ML model is additive and can be exactly described. Studying interaction effects of ML models is challenging. The main XAI approaches are: This post is mainly about the third approach. Its beauty is that we get information about all interactions.…
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Model Diagnostics in Python
Version 1.0.0 of the new Python package for model-diagnostics was just released on PyPI.
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Geographic SHAP
“R Python” continued… Geographic SHAP
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Quantiles And Their Estimation
Applied statistics is dominated by the ubiquitous mean. For a change, this post is dedicated to quantiles. I will give my best to provide a good mix of theory and practical examples. While the mean describes only the central tendency of a distribution or random sample, quantiles are able to describe the whole distribution. They…
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SHAP + XGBoost + Tidymodels = LOVE
tidymodels and shapviz to explain XGBoost models
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Dplyr-style without dplyr
How to get “dplyr” feeling without “dplyr”
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Interpret Complex Linear Models with SHAP within Seconds
Peaking into richly parametrized linear models with SHAP? Yes!
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The Unfairness of AI Fairness
Fairness in Artificial Intelligence (AI) and Machine Learning (ML) is a recent and hot topic. As ML models are used in insurance pricing, the fairness topic also applies there. Just last month, Lindholm, Richman, Tsanakas and Wüthrich published a discussion paper on this subject that sheds new light on established AI fairness criteria. This post…
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Kernel SHAP in R and Python
“R Python” continued… Kernel SHAP
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Kernel SHAP
Standard Kernel SHAP has arrived in R. We show how well it plays together with deep learning in Keras
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shapviz goes H2O
The “shapviz” package now plays well together with H2O.
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Visualize SHAP Values without Tears
Visualize SHAP values without tears.