What is machine learning interpretability in Python?

What is machine learning interpretability in Python?

Machine learning interpretability refers to the methods and techniques used to understand how machine learning models make decisions. In Python, this can involve using libraries like SHAP or LIME to explain model predictions.

Why is interpretability important in machine learning?

Interpretability is crucial because it helps build trust in models, allows for debugging, and ensures compliance with regulations, especially in sensitive areas like healthcare and finance.

Can you give me an example of how to use SHAP in Python?

Sure! First, you need to install the SHAP library. Then, you can use it to explain predictions from your model. For example:

import shap

# Assuming you have a trained model and data
explainer = shap.Explainer(model)
shap_values = explainer(data)
shap.summary_plot(shap_values, data)