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Data Science & ML track

Model Explainability

SHAP, LIME, partial dependence and communicating model behavior.

6 concepts3 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

Explainability Overview

Concept · Hands-on lab included

2

Feature Importance

Concept · Hands-on lab included

3

SHAP Values

Concept · Hands-on lab included

4

Surrogate Models & LIME

Concept · Hands-on lab included

5

Partial Dependence & ICE Plots

Concept · Hands-on lab included

6

Counterfactual Explanations

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

Rank Features Honestly

30 min · intermediate · Feature Importance

Open a Model with SHAP

40 min · advanced · SHAP Values

Draw PDP and ICE Curves

30 min · intermediate · Partial Dependence & ICE Plots

Continue

Next modules

Keep your learning streak going with related modules.

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Mathematics

Linear algebra, calculus and notation needed for machine learning.

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Statistics

Distributions, estimators, hypothesis testing and statistical inference.

0%

Python

Scientific Python stack: environments, typing, numpy-style thinking.

DevOpsForge - Data Science & ML · Model Explainability
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