Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.utils.typing.JuModelLike¶

class julearn.utils.typing.JuModelLike(*args, **kwargs)¶

Class for jumodel-like.

get_needed_types()¶

Get the column types needed by the estimator.

Returns:: The column types needed by the estimator.

get_apply_to()¶

Get the column types the estimator applies to.

Returns:: The column types the estimator applies to.

fit(X, y, **kwargs)¶

Fit estimator.

Parameters:

X (ndarray | DataFrame | Series) – The features to use.
y (Series) – The target to use.
**kwargs (Any) – Extra keyword arguments.

Returns:

The fitted estimator.

get_params(deep=True)¶

Get params.

Parameters:: deep (bool, default: True) – Whether to get in a deep fashion (default True).
Returns:: The parameters.

predict(X)¶

Predict using the model.

Parameters:: X (DataFrame) – The data to predict on.
Returns:: The predictions.

score(X, y, sample_weight=None)¶

Score the model.

Parameters:

X (DataFrame) – The data to predict on.
y (ndarray | DataFrame | Series) – The true target values.
sample_weight (ndarray | DataFrame | Series | None, default: None) – Sample weights to use when computing the score (default None).

Returns:

The score.

set_params(**params)¶

Set params.

Parameters:: **params (Any) – The parameters to set.
Returns:: Estimator with set parameters.

classes_: ndarray¶