Ultimate Model Interpretability and Explainability Tutorial…

PDP Coverage: How well does the PDP cover the range of potential features?

Conditional PDP Accuracy: Assessing whether the conditional PDP accurately reflects the true relationship between features and the predicted outcome – again often requiring expert evaluation alongside quantitative measures of fit (e.g., R-squared).

3. Model Agnostic Explanations & Surrogate Models:

6. Visualization Techniques for XAI

7. Frameworks and Tools for Implementing XAI

8. Ethical Considerations & Best Practices

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Frequently asked questions

What is LIME (Local Interpretable Model-Agnostic Explanations)?

LIME (Local Interpretable Model-Agnostic Explanations): LIME explains individual predictions by fitting a locally interpretable model around them – typically a linear regression model. This allows you to understand why the model made that specific prediction in that particular instance.

How does Integrated Gradients calculate explanations?

Integrated Gradients calculates the integral of gradients along a path from a baseline to the input feature, providing an explanation based on the gradient information. This approach helps quantify the contribution of each feature to the prediction.

What does the (H2) Visual illustrate regarding SHAP values?

(H2) Visual: Illustration of SHAP Value Calculation – this visual demonstrates how SHAP values are computed by tracing the impact of each feature on the prediction through the model's layers.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation