AI Simplified: SHAP Values in Machine Learning

Показать описание

Stay connected with DataRobot!

DataRobot

Рекомендации по теме

Комментарии

Thank you Mark for a great and simple example of SHAP value!

I used SHAP as a tool for feature selection for my XGBoost decision tree model. After obtaining the feature importance, I noticed that the SHAP values of some features are equal to zero. After investigating into more details, I found that the features with zero SHAP value are collinear. For example, if features A and B are highly correlated, the SHAP value of B is then set to zero.

However, from my understand, Shapley value uses cooperative game theory to compute the contribution of each signal. So, in case of multicollinearity, shouldn't A and B have the same value of Shapley value? Also, if the Shapley value can handle multicollinearity, how does SHAP select the signal and ignore the other correlated features?

suratasvapoositkul

I can apply shap library and interpret the chart but what is final report out if it ??? Like what management / user expect from it ??? I can't see this chart to non-technical person . is there any report can be generated to draw any conclusion

pra

AI Simplified: SHAP Values in Machine Learning

AI Simplified: SHAP Values in Machine Learning

SHAP values for beginners | What they mean and their applications

Explainable AI explained! | #4 SHAP

Shapley Values : Data Science Concepts

SHAP Values: An Overview

SHAP with Python (Code and Explanations)

Explainable AI, Session 5: Intro to SHAP

Machine Learning Model Explainability with SHAP Values

The mathematics behind Shapley Values

SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems

Explainable AI with Shapley Values (Part 3: KernelSHAP)

Shapley Values Explained | Interpretability for AI models, even LLMs!

Shapley Additive Explanations (SHAP)

Explainable AI explained! | #3 LIME

Interpretable vs Explainable Machine Learning

How to interpret #shapley Summary Plot | #beeswarm Plot Interpretation | #ExplainableAI #XAI

How SHAP value is calculated? It is not hard! (simple example)

Episode 63. Explainable AI: SHAP (SHapley Additive exPlanations)

SHAP Values Unlock AI Model Insights

05e Machine Learning: Shapley Value

The Kernel Trick in Support Vector Machine (SVM)

Using SHAP to explain models

Explain Machine Learning Models with SHAP in Python

AI Simplified: Model Interpretability