Tag: R
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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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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…
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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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Geographic SHAP
“R Python” continued… Geographic SHAP
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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”