Mindblown: a blog about philosophy.
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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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Interactive Plotting Backend for Model-Diagnostics
With its newest release 1.1.0, the Python package model-diagnostics got the concept of a plotting backend. Before this release, all plots were constructed with matplotlib. This is still the default. But additionally, the user can now select plotly, if it is installed. There are 2 ways to specify the plotting backend explicitly The context manager…
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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.
Got any book recommendations?