custom-sklearn-estimator

To create a custom scikit-learn estimator that passes check_estimator, implement __init__ with keyword arguments, define fit and predict methods with input validation via check_array, and append trailing underscores to learned attributes like coef_.

Scikit-learn requires __init__ to use keyword arguments with defaults assigned to matching attributes, and fit(self, X, y=None, **kwargs) must validate inputs, set learned attributes with trailing underscores, and return self.

To make a custom sklearn estimator work in pipelines, implement get_params and set_params, use check_is_fitted before predictions, and validate inputs with check_array to ensure full compatibility with pipeline tooling.

Scikit-learn requires custom estimator predict and transform methods to call check_is_fitted to verify the model is trained, and check_array to validate input data before generating outputs.

Handle random_state in a custom scikit-learn estimator by accepting it as a keyword argument in __init__ and passing it to check_random_state during fit to ensure reproducible randomness across pipeline executions.

Custom sklearn estimator learned attributes fail validation checks if they lack trailing underscores. You must name learned attributes like classes_ or coef_ to distinguish them from constructor parameters.