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The Limitations of SHAP

Towards Data Science

How SHAP is impacted by feature dependencies, causal inference and human biases Continue reading on Towards Data Science

SHAP Values

Kaggle Learn Courses

Introduction You've seen (and used) techniques to extract general insights from a machine learning model. But what if you want to break down how the model works for an individual prediction? SHAP Val...

SHAP explained the way I wish someone explained it to me

Towards Data Science

SHAP — which stands for SHapley Additive exPlanations — is probably the state of the art in Machine Learning explainability. This algorithm was first published in 2017 by Lundberg and Lee (here is…

Analysing Interactions with SHAP

Towards Data Science

SHAP values are used to explain individual predictions made by a model. It does this by giving the contributions of each factor to the final prediction. SHAP interaction values extend on this by…

June Edition: Get into SHAP

Towards Data Science

The ins and outs of a powerful explainable-AI approach Photo by Héctor J. Rivas on Unsplash The power and size of machine learning models have grown to new heights in recent years. With greater compl...

Kernel SHAP

R-bloggers

Standard Kernel SHAP has arrived in R. We show how well it plays together with deep learning in Keras Continue reading: Kernel SHAP

SHAP Part 2: Kernel SHAP

Analytics Vidhya

Kernel SHAP is a model agnostic method to approximate SHAP values using ideas from LIME and Shapley values. This is my second article on SHAP. Refer to my previous post here for a theoretical…

Advanced Uses of SHAP Values

Kaggle Learn Courses

Recap We started by learning about permutation importance and partial dependence plots for an overview of what the model has learned. We then learned about SHAP values to break down the components of...

SHAP Part 3: Tree SHAP

Analytics Vidhya

Tree SHAP is an algorithm to compute exact SHAP values for Decision Trees based models. SHAP (SHapley Additive exPlanation) is a game theoretic approach to explain the output of any machine learning…

Geographic SHAP

R-bloggers

"R Python" continued... Geographic SHAP Continue reading: Geographic SHAP

Casual SHAP values: A possible improvement of SHAP values

Towards Data Science

An introduction and a case study Image by Evan Dennis As explained in my previous post, the framework of SHAP values, widely used for machine learning explainability has unfortunately failed to refle...

Introduction to SHAP Values and their Application in Machine Learning

Towards Data Science

Learn how the SHAP library works under the hood Continue reading on Towards Data Science

How to avoid the Machine Learning blackbox with SHAP

Towards Data Science

Blackbox algorithms can be loosely defined as algorithms whose output is not easily interpretable or is non-interpretable altogether. Meaning you get an output from an input but you don’t understand…

Why SHAP values might not be perfect

Towards Data Science

Two examples of the weak points of SHAP values and an overview of possible solutions SHAP values seem to remove the trade-off between the complexity of machine learning models and the difficulty of i...

Introduction to SHAP with Python

Towards Data Science

For a given prediction, SHAP values can tell us how much each factor in a model has contributed to the prediction. We can also aggregate SHAP values to understand how the model makes predictions in…

Visualize SHAP Values without Tears

R-bloggers

Visualize SHAP values without tears. Continue reading: Visualize SHAP Values without Tears

SHAP for Binary and Multiclass Target Variables

Towards Data Science

A guide to the code and interpreting SHAP plots when your model predicts a categorical target variable Photo by Nika Benedictova on Unsplash SHAP values give the contribution of a model feature to a ...

Idea Behind LIME and SHAP

Towards Data Science

In machine learning, there has been a trade-off between model complexity and model performance. Complex machine learning models e.g. deep learning (that perform better than interpretable models e.g…

shapviz goes H2O

R-bloggers

The "shapviz" package now plays well together with H2O. Continue reading: shapviz goes H2O

New SHAP Plots: Violin and Heatmap

Towards Data Science

What the plots in SHAP version 0.42.1 can tell you about your model Continue reading on Towards Data Science

SHAP (SHapley Additive exPlanations)

Christophm Interpretable Machine Learning Book

SHAP (SHapley Additive exPlanations) by Lundberg and Lee (2017) 69 is a method to explain individual predictions. SHAP is based on the game theoretically optimal Shapley values . There are two reasons...

SHAP Part 1: An Introduction to SHAP

Analytics Vidhya

Before we get to the “why” part of the question, let’s understand what is meant by Interpretability. While there is no mathematical definition for interpretability, a heuristic definition like the…

Hate Black-box Models? Time to Change That With SHAP

Towards Data Science

Learn the ins and outs of explaining any black-box models with SHAP and Shapley values in this comprehensive model explainability guide.

Explain Your Model with the SHAP Values

Towards Data Science

Is your highly-trained model easy to understand? A sophisticated machine learning algorithms usually can produce accurate predictions, but its notorious “black box” nature does not help adoption at…