evalml.pipelines.components.TargetEncoder.__init__

TargetEncoder.__init__(cols=None, smoothing=1.0, handle_unknown='value', handle_missing='value', random_state=None, random_seed=0, **kwargs)[source]

Initializes a transformer that encodes categorical features into target encodings.

Parameters

  • cols (list) – Columns to encode. If None, all string columns will be encoded, otherwise only the columns provided will be encoded. Defaults to None

  • smoothing (float) – The smoothing factor to apply. The larger this value is, the more influence the expected target value has on the resulting target encodings. Must be strictly larger than 0. Defaults to 1.0

  • handle_unknown (string) – Determines how to handle unknown categories for a feature encountered. Options are ‘value’, ‘error’, nd ‘return_nan’. Defaults to ‘value’, which replaces with the target mean

  • handle_missing (string) – Determines how to handle missing values encountered during fit or transform. Options are ‘value’, ‘error’, and ‘return_nan’. Defaults to ‘value’, which replaces with the target mean

evalml.pipelines.components.TargetEncoder evalml.pipelines.components.TargetEncoder.clone