MultiClassTargetEncoder

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

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

static score_func(candidates, target)[source]

Parameters

Return type

int

Returns

index of best encoder.

fit_transform(dataset)[source]

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)[source]

Transform categorical dataset to target encoding.

Parameters: dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of categorical features.
Return type: NumpyDataset
Returns: Numpy dataset with encoded labels.