Tag: Python
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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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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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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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Histograms, Gradient Boosted Trees, Group-By Queries and One-Hot Encoding
This post shows how filling histograms can be done in very different ways thereby connecting very different areas: from gradient boosted trees to SQL queries to one-hot encoding. Let’s jump into it! Modern gradient boosted trees (GBT) like LightGBM, XGBoost and the HistGradientBoostingRegressor of scikit-learn all use two techniques on top of standard gradient boosting:…
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Kernel SHAP in R and Python
“R Python” continued… Kernel SHAP