# Intro to Model Explainability

Learn about explainable methods for understanding model decisions.

As businesses across sectors implement ML and AI, the need for transparent decision-making grows increasingly important. The problem with black-box models (neural networks, large language models, etc.) is that their decision process is entirely opaque and unauditable. **Model explainability** has evolved as a subfield to combat this problem.

## Explainability vs. interpretability

Simply put, explainability attempts to provide some clarity into *how* an ML algorithm makes its decision. **Interpretability** is the ability to have clarity into *why* an ML algorithm made a decision. The difference is subtle but has significant consequences, notably that explainability is just one piece of interpretability. This is best illustrated in an example.

Consider again a lending algorithm that attempts to classify applicants as either able or unable to repay a loan. Let’s assume we run two different models: a logistic regression and a random forest.

### Logistic regressions

With a **logistic regression** model, it’s possible to retrieve the exact parameters that went into making the prediction. Because logistic regressions are essentially linear models and have a set formula, anyone can instantly understand properties, such as which features were the most relevant and (much more difficult) the effect of changes to variables on the output.

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