bigframes.ml.preprocessing.LabelEncoder#

class bigframes.ml.preprocessing.LabelEncoder(min_frequency: int | None = None, max_categories: int | None = None)[source]#

Encode target labels with value between 0 and n_classes-1.

This transformer should be used to encode target values, i.e. y, and not the input X.

Parameters:

min_frequency (Optional[int], default None) – Specifies the minimum frequency below which a category will be considered infrequent. Default None. int: categories with a smaller cardinality will be considered infrequent as ßindex 0.
max_categories (Optional[int], default None) – Specifies an upper limit to the number of output features for each input feature when considering infrequent categories. If there are infrequent categories, max_categories includes the category representing the infrequent categories along with the frequent categories. Default None. Set limit to 1,000,000.

Attributes

`FREQUENCY_THRESHOLD_DEFAULT`
`TOP_K_DEFAULT`

Methods

`__init__`([min_frequency, max_categories])
`fit`(y)	Fit label encoder.
`fit_transform`(y)
`get_params`([deep])	Get parameters for this estimator.
`to_gbq`(model_name[, replace])	Save the transformer as a BigQuery model.
`transform`(y)	Transform y using label encoding.