When working with machine learning models in Python, it is common to use the BaseEstimator class from the sklearn.base module. This class provides a base implementation for all estimators in scikit-learn, allowing you to easily create your own custom estimators.

Solution 1: Inheriting from BaseEstimator

The first solution involves creating a new class that inherits from the BaseEstimator class. This allows you to override the necessary methods and customize the behavior of your estimator.

from sklearn.base import BaseEstimator

class MyEstimator(BaseEstimator):
    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2

    def fit(self, X, y=None):
        # Implement your fitting logic here
        pass

    def predict(self, X):
        # Implement your prediction logic here
        pass

# Example usage
estimator = MyEstimator(param1=1, param2=2)
estimator.fit(X_train, y_train)
predictions = estimator.predict(X_test)

In this solution, you create a new class called MyEstimator that inherits from BaseEstimator. You can then override the fit and predict methods to implement your own fitting and prediction logic. The __init__ method allows you to initialize any necessary parameters for your estimator.

Solution 2: Using mixins

Another approach is to use mixins, which are classes that provide additional functionality to other classes through multiple inheritance. In this case, you can create a mixin class that extends the BaseEstimator class and add the necessary methods.

from sklearn.base import BaseEstimator

class MyMixin:
    def fit(self, X, y=None):
        # Implement your fitting logic here
        pass

    def predict(self, X):
        # Implement your prediction logic here
        pass

class MyEstimator(BaseEstimator, MyMixin):
    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2

# Example usage
estimator = MyEstimator(param1=1, param2=2)
estimator.fit(X_train, y_train)
predictions = estimator.predict(X_test)

In this solution, you create a mixin class called MyMixin that provides the fit and predict methods. Then, you create a new class called MyEstimator that inherits from both BaseEstimator and MyMixin. This allows you to use the methods from both classes in your estimator.

Solution 3: Using composition

The third solution involves using composition instead of inheritance. Instead of inheriting from BaseEstimator, you can create a class that contains an instance of BaseEstimator and delegates the necessary methods to it.

from sklearn.base import BaseEstimator

class MyEstimator:
    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2
        self.estimator = BaseEstimator()

    def fit(self, X, y=None):
        return self.estimator.fit(X, y)

    def predict(self, X):
        return self.estimator.predict(X)

# Example usage
estimator = MyEstimator(param1=1, param2=2)
estimator.fit(X_train, y_train)
predictions = estimator.predict(X_test)

In this solution, you create a class called MyEstimator that contains an instance of BaseEstimator. The fit and predict methods delegate the calls to the corresponding methods of the BaseEstimator instance. This allows you to customize the behavior of your estimator without directly inheriting from BaseEstimator.

Among these three options, the best choice depends on your specific requirements and the complexity of your custom estimator. In general, if you need full control over the behavior of your estimator and want to override multiple methods, inheriting from BaseEstimator (Solution 1) is a good option. If you only need to add a few methods, using mixins (Solution 2) can provide a more modular approach. Finally, if you prefer composition over inheritance or want to customize the behavior of an existing estimator, using composition (Solution 3) is a suitable choice.