MultiClassTargetEncoder

class lightautoml.transformers.categorical.MultiClassTargetEncoder(alphas=(0.5, 1.0, 2.0, 5.0, 10.0, 50.0, 250.0, 1000.0))

Bases: LAMLTransformer

Out-of-fold target encoding for multiclass task.

Limitation:

• Required .folds attribute in dataset - array of int from 0 to n_folds-1.

• Working only after label encoding

property features

List of features.

static score_func(candidates, target)

Choose the best encoder.

Parameters:

• candidates (ndarray) – np.ndarray.

• target (ndarray) – np.ndarray.

Return type:

int

Returns:

index of best encoder.

fit_transform(dataset)

Estimate label frequencies and create encoding dicts.

Parameters:

dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of categorical label encoded features.

Return type:

NumpyDataset

Returns:

NumpyDataset - target encoded features.

transform(dataset)

Transform categorical dataset to target encoding.

Parameters:

dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of categorical features.

Return type:

Union[NumpyDataset, CSRSparseDataset]

Returns:

Numpy dataset with encoded labels.