SHAP with CatBoostClassifier for Categorical Features | Python Tutorial

Показать описание

Combining CatBoost and SHAP can provide powerful insight into your machine learning models. Especially, when you are working with categorical features.

With other modelling packages, we need to first transform categorical features using one-hot encodings. The problem is that each binary variable will have its own SHAP value. This makes it difficult to see the overall contribution of the original categorical feature.

Using Python, we will apply SHAP to a CatBoostClassifier. We will see the model's advantage over other libraries is that it can handle non-numerical data without transforming them. This means that SHAP values of a CatBoost model are easy to interpret as there will only be one SHAP value for each categorical feature.

🚀 SHAP Course 🚀
The first 20 people to use the coupon code "CATSHAP24" will get 100% off!

🚀 Free XAI Courses 🚀

🚀 GitHub Link with Code 🚀

🚀 Companion article with link to code (no-paywall link): 🚀

🚀 Useful playlists 🚀

🚀 Get in touch 🚀

🚀 Chapters 🚀
00:00 Introduction
01:40 Python Code
07:03 When to use CatBoost?

A Data Odyssey

Рекомендации по теме

Комментарии

🚀 SHAP Course 🚀
The first 20 people to use the coupon code "CATSHAP24" will get 100% off!

adataodyssey

Hey! Thank you for the video.

Just a note: XGBoost now automatically deals with categorical features like Catboost. You just need to pass enable_categorical=True when creating the XGBClassifier!

FP-mgqk

Thank you! You clearly have a talent for explaining complex topics. At my university, it feels like the XAI course is covering almost exclusively topics from your channel! I appreciate your work and hope you find the time and enjoy making these videos! Best, Tim

nrthern_lights

How can we get shap value for each category in Catboost model like we get after one hot encoding in XGboost?

SnehaChoudhary-ng

SHAP with CatBoostClassifier for Categorical Features | Python Tutorial

SHAP with CatBoostClassifier for Categorical Features | Python Tutorial

What is Category Boosting (CatBoost) in Machine Learning?

Master CatBoost Classifier: Step-by-Step Python Machine Learning Tutorial for Beginners

CatBoost | Interpret CatBoost models: built-in tools for understanding model predictions

Classification Model Selection in Python : Powerful Classification Code Templates In Action - Step 4

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in R - Step 4

Classification Model Selection in Python : Powerful Classification Code Templates In Action - Step 1

Learn Machine Learning | Data Preprocessing in Python - Step 5 | Encoding Categorical Data

Learn Machine Learning | Data Preprocessing in R - Step 5 | Encoding Categorical Data

Classification Model Selection in Python : Powerful Classification Code Templates In Action - Step 2

Classification Model Selection in Python : Powerful Classification Code Templates In Action - Step 3

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in Python - Step 4

Машинное обучение. Семинар 7. Shap values

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in Python - Step 6

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in Python - Step 7

Evaluating Classification Models Performance : CAP Curve Analysis

Mentor Talks : Catboost Classifier Algorithm

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in Python - Step 5

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in Python - Step 1

Evaluating Classification Models Performance : CAP Curve

Learn Machine Learning | Data Preprocessing - Feature Scaling

Learn Machine Learning | Reinforcement Learning - Upper Confidence Bound (UCB) in Python - Step 2

Learn Machine Learning | R Classification Template: A Comprehensive Guide to Classification in R

Learn Machine Learning | Random Forest Classification in Python - Step 1