Category: Machine Learning
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Effect Plots in Python and R
This post introduces new Python and R functionality how to get a quick summary of any model.
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Explaining a Causal Forest
Causal forests model treatment effect inhomogeneity. We use XAI tools to interpret such model to see which features are associated with the treatment effect.
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Out-of-sample Imputation with {missRanger}
Multivariate imputations with missRanger.
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SHAP Values of Additive Models
This post investigates properties of SHAP values of additive models.
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A Tweedie Trilogy — Part III: From Wrights Generalized Bessel Function to Tweedie’s Compound Poisson Distribution
This trilogy celebrates the 40th birthday of Tweedie distributions in 2024 and highlights some of their very special properties.
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A Tweedie Trilogy — Part II: Offsets
This trilogy celebrates the 40th birthday of Tweedie distributions in 2024 and highlights some of their very special properties.
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A Tweedie Trilogy — Part I: Frequency and Aggregration Invariance
This trilogy celebrates the 40th birthday of Tweedie distributions in 2024 and highlights some of their very special properties.
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Building Strong GLMs in Python via ML + XAI
We use Python to craft a strong GLM by insights from a boosted trees model.
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ML + XAI -> Strong GLM
In this post, we improve a simple GLM by insights from a boosted trees model.
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Explain that tidymodels blackbox!
In this post you will learn how to explain a {tidymodels} blackbox with classic XAI and SHAP.
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Permutation SHAP versus Kernel SHAP
When do the two methods agree? When not?
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Interactions – where are you?
This question sends shivers down the poor modelers spine… The {hstats} R package introduced in our last post measures their strength using Friedman’s H-statistics, a collection of statistics based on partial dependence functions. On Github, the preview version of {hstats} 1.0.0 out – I will try to bring it to CRAN in about one week…