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TargetEncoder

Target Encoder for regression and classification targets.

Each category is encoded based on a shrunk estimate of the average target values for observations belonging to the category. The encoding scheme mixes the global target mean with the target mean conditioned on the value of the category. [MIC]

TargetEncoder considers missing values, such as np.nan or undefined, as another category and encodes them like any other category. Categories that are not seen during fit are encoded with the target mean, i.e. target\_mean\_.

For a demo on the importance of the TargetEncoder internal cross-fitting, see ref:sphx\_glr\_auto\_examples\_preprocessing\_plot\_target\_encoder\_cross\_val.py. For a comparison of different encoders, refer to Comparing Target Encoder with Other Encoders. Read more in the User Guide.

Python Reference

Constructors

constructor()

Signature

new TargetEncoder(opts?: object): TargetEncoder;

Parameters

Name Type Description
opts? object -
opts.categories? "auto" Categories (unique values) per feature: Default Value 'auto'
opts.cv? number Determines the number of folds in the cross fitting strategy used in fit\_transform. For classification targets, StratifiedKFold is used and for continuous targets, KFold is used. Default Value 5
opts.random_state? number When shuffle is true, random\_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See Glossary.
opts.shuffle? boolean Whether to shuffle the data in fit\_transform before splitting into folds. Note that the samples within each split will not be shuffled. Default Value true
opts.smooth? number | "auto" The amount of mixing of the target mean conditioned on the value of the category with the global target mean. A larger smooth value will put more weight on the global target mean. If "auto", then smooth is set to an empirical Bayes estimate. Default Value 'auto'
opts.target_type? "auto" | "binary" | "continuous" Type of target. Default Value 'auto'

Returns

TargetEncoder

Defined in: generated/preprocessing/TargetEncoder.ts:27

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/preprocessing/TargetEncoder.ts:131

fit()

Fit the TargetEncoder to X and y.

Signature

fit(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] The data to determine the categories of each feature.
opts.y? ArrayLike The target data used to encode the categories.

Returns

Promise<any>

Defined in: generated/preprocessing/TargetEncoder.ts:148

fit_transform()

Fit TargetEncoder and transform X with the target encoding.

Signature

fit_transform(opts: object): Promise<ArrayLike[]>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] The data to determine the categories of each feature.
opts.y? ArrayLike The target data used to encode the categories.

Returns

Promise<ArrayLike[]>

Defined in: generated/preprocessing/TargetEncoder.ts:188

get_feature_names_out()

Get output feature names for transformation.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.input_features? any Input features.

Returns

Promise<any>

Defined in: generated/preprocessing/TargetEncoder.ts:228

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.routing? any A MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/preprocessing/TargetEncoder.ts:266

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name Type
py PythonBridge

Returns

Promise<void>

Defined in: generated/preprocessing/TargetEncoder.ts:85

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.transform? "default" | "pandas" Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/preprocessing/TargetEncoder.ts:303

transform()

Transform X with the target encoding.

Signature

transform(opts: object): Promise<ArrayLike[]>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] The data to determine the categories of each feature.

Returns

Promise<ArrayLike[]>

Defined in: generated/preprocessing/TargetEncoder.ts:336

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/TargetEncoder.ts:25

_isInitialized

boolean = false

Defined in: generated/preprocessing/TargetEncoder.ts:24

_py

PythonBridge

Defined in: generated/preprocessing/TargetEncoder.ts:23

id

string

Defined in: generated/preprocessing/TargetEncoder.ts:20

opts

any

Defined in: generated/preprocessing/TargetEncoder.ts:21

Accessors

categories_

The categories of each feature determined during fitting or specified in categories (in order of the features in X and corresponding with the output of transform).

Signature

categories_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/preprocessing/TargetEncoder.ts:394

encodings_

Encodings learnt on all of X. For feature i, encodings\_\[i\] are the encodings matching the categories listed in categories\_\[i\].

Signature

encodings_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/preprocessing/TargetEncoder.ts:369

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/TargetEncoder.ts:494

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/preprocessing/TargetEncoder.ts:469

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/preprocessing/TargetEncoder.ts:72

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/preprocessing/TargetEncoder.ts:76

target_mean_

The overall mean of the target. This value is only used in transform to encode categories.

Signature

target_mean_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/preprocessing/TargetEncoder.ts:444

target_type_

Type of target.

Signature

target_type_(): Promise<string>;

Returns

Promise<string>

Defined in: generated/preprocessing/TargetEncoder.ts:419