LabelBinarizer

Binarize labels in a one-vs-all fashion.

Several regression and binary classification algorithms are available in scikit-learn. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme.

At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multi-class labels to binary labels (belong or does not belong to the class). LabelBinarizer makes this process easy with the transform method.

At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. LabelBinarizer makes this easy with the inverse_transform method.

Constructors

constructor()

Signature

new LabelBinarizer(opts?: object): LabelBinarizer;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.neg_label?`	`number`	Value with which negative labels must be encoded. `Default Value` `0`
`opts.pos_label?`	`number`	Value with which positive labels must be encoded. `Default Value` `1`
`opts.sparse_output?`	`boolean`	True if the returned array from transform is desired to be in sparse CSR format. `Default Value` `false`

Returns

LabelBinarizer

Defined in: generated/preprocessing/LabelBinarizer.ts:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/LabelBinarizer.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/preprocessing/LabelBinarizer.ts:26 (opens in a new tab)

_py

PythonBridge

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

id

string

Defined in: generated/preprocessing/LabelBinarizer.ts:22 (opens in a new tab)

opts

any

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

Accessors

classes_

Holds the label for each class.

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/LabelBinarizer.ts:311 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/preprocessing/LabelBinarizer.ts:55 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/preprocessing/LabelBinarizer.ts:59 (opens in a new tab)

sparse_input_

True if the input data to transform is given as a sparse matrix, false otherwise.

Signature

sparse_input_(): Promise<boolean>;

Returns

Promise<boolean>

Defined in: generated/preprocessing/LabelBinarizer.ts:361 (opens in a new tab)

y_type_

Represents the type of the target data as evaluated by utils.multiclass.type_of_target. Possible type are ‘continuous’, ‘continuous-multioutput’, ‘binary’, ‘multiclass’, ‘multiclass-multioutput’, ‘multilabel-indicator’, and ‘unknown’.

Signature

y_type_(): Promise<string>;

Returns

Promise<string>

Defined in: generated/preprocessing/LabelBinarizer.ts:336 (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/LabelBinarizer.ts:110 (opens in a new tab)

fit()

Fit label binarizer.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.y?`	`ArrayLike`	Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification.

Returns

Promise<any>

Defined in: generated/preprocessing/LabelBinarizer.ts:127 (opens in a new tab)

fit_transform()

Fit label binarizer/transform multi-class labels to binary labels.

The output of transform is sometimes referred to as the 1-of-K coding scheme.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.y?`	`any`	Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/LabelBinarizer.ts:162 (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/LabelBinarizer.ts:68 (opens in a new tab)

inverse_transform()

Transform binary labels back to multi-class labels.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.Y?`	`ArrayLike`	Target values. All sparse matrices are converted to CSR before inverse transformation.
`opts.threshold?`	`number`	Threshold used in the binary and multi-label cases. Use 0 when `Y` contains the output of decision_function (classifier). Use 0.5 when `Y` contains the output of predict_proba. If `undefined`, the threshold is assumed to be half way between neg_label and pos_label.

Returns

Promise<any>

Defined in: generated/preprocessing/LabelBinarizer.ts:195 (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/LabelBinarizer.ts:243 (opens in a new tab)

transform()

Transform multi-class labels to binary labels.

The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.y?`	`any`	Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/LabelBinarizer.ts:278 (opens in a new tab)

KNNImputer LabelEncoder