Publications by Michael Mayer
Let the flashlight shine with plotly
There are different R packages devoted to model agnostic interpretability, DALEX and iml being among the best known. In 2019, I added flashlight for a couple of reasons: Its explainers work with case weights.Multiple explainers can be combined to a multi-explainer.Stratified calculation is possible. Since almost all plots in flashlight ar...
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Visualize SHAP Values without Tears
SHAP (SHapley Additive exPlanations, Lundberg and Lee, 2017) is an ingenious way to study black box models. SHAP values decompose – as fair as possible – predictions into additive feature contributions. When it comes to SHAP, the Python implementation is the de-facto standard. It not only offers many SHAP algorithms, but also provides beauti...
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shapviz goes H2O
In a recent post, I introduced the initial version of the “shapviz” package. Its motto: do one thing, but do it well: visualize SHAP values. The initial community feedback was very positive, and a couple of things have been improved in version 0.2.0. Here the main changes: “shapviz” now works with tree-based models of the h2o package in R...
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Illustrating The Central Limit Theorem
Lost in Translation between R and Python 1 This is the first article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Python, the Python tab will be ...
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Covid-19 Deaths per Mio
Lost in Translation between R and Python 2 Hello again! This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Python, the Python ...
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A Beautiful Regression Formula
Lost in Translation between R and Python 4 Hello statistics aficionados This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Pyt...
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A Curious Fact on the Diamonds Dataset
Lost in Translation between R and Python 5 Hello regression world This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Python, t...
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Strong random forests with XGBoost
Lost in Translation between R and Python 6 Hello random forest friends This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Pyth...
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SHAP Analysis in 9 Lines
Hello ML world Recently, together with Yang Liu, we have been investing some time to extend the R package SHAPforxgboost. This package is designed to make beautiful SHAP plots for XGBoost models, using the native treeshap implementation shipped with XGBoost. Some of the new features of SHAPforxgboost Added support for LightGBM models, using the...
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Random Forests with Monotonic Constraints
Lost in Translation between R and Python 7 Hello random forest friends This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Pyth...
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