6Python libraries to help you Interpret Machine Learning Models #datatechnology #datascience

Struggling to interpret your machine learning models? Understanding how your models make decisions is crucial for building trust and improving performance.

Check out these SIX Python libraries designed to shed light on your models' inner working. 👇

📌 Save this reel for later if you’re in a hurry ⏰

1️⃣ Scikit-learn (the classic!) goes beyond coefficients with permutation importance and partial dependency plots.

2️⃣ treeinterpreter delves deep into decision trees and random forests.

3️⃣ eli5 explains not only GBMs but also CatBoost, lightGBM, and more!

4️⃣ lime provides local explanations for black box models using LIME.

5️⃣ SHAP utilizes SHAP values for model output explanations.

6️⃣ interpret offers a toolbox for explainable models and even introduces Eplainable Boosting Machines (EBMs).

Which packages are already part of your development toolkit? Share in the comments below and let's discuss! 👇

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🏷️ #MachineLearning #DataScience #AI #ML #MLModels #ModelBuilding #DataScientist #Python #MLLibraries #ModelInterpretability #PythonLibraries