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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/TargetEncoder.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/preprocessing/TargetEncoder.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/preprocessing/TargetEncoder.ts:23 (opens in a new tab)

id

string

Defined in: generated/preprocessing/TargetEncoder.ts:20 (opens in a new tab)

opts

any

Defined in: generated/preprocessing/TargetEncoder.ts:21 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/preprocessing/TargetEncoder.ts:72 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/preprocessing/TargetEncoder.ts:76 (opens in a new tab)

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 (opens in a new tab)

target_type_

Type of target.

Signature

target_type_(): Promise<string>;

Returns

Promise<string>

Defined in: generated/preprocessing/TargetEncoder.ts:419 (opens in a new tab)

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