What are abstract base classes in Python and how are they used in machine learning?

What are abstract base classes in Python?

Abstract Base Classes (ABCs) in Python are a way to define common interfaces for a group of related classes. They allow you to enforce that certain methods must be implemented in any subclass.

How are they useful in machine learning?

In machine learning, ABCs can be used to define a standard interface for different models, ensuring that they all implement methods like fit, predict, and score. This promotes code reusability and consistency.

Can you give an example of using ABCs in a machine learning context?

Sure! You can create an abstract class Model with abstract methods like fit and predict. Then, subclasses like LinearRegression and DecisionTree would implement these methods, ensuring they adhere to the defined interface.

What are the benefits of using ABCs over regular classes?

ABCs provide a clear contract for subclasses, making your code more organized and easier to maintain. They also help catch errors at the design stage, as subclasses must implement the required methods.