Interpretable Models
Interpretable models are machine learning models that are designed to be easily understood and interpreted by humans. These models are particularly useful in situations where it is important to understand how the model arrived at its predictions or decisions. Interpretable models are often used in fields such as healthcare, finance, and law, where the consequences of a model's decisions can have significant real-world impact. Examples of interpretable models include decision trees, linear regression models, and rule-based models. These models are often contrasted with black box models, such as deep neural networks, which are more difficult to interpret due to their complex internal workings.
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