DummyClassifier

DummyClassifier makes predictions that ignore the input features.

This classifier serves as a simple baseline to compare against other more complex classifiers.

The specific behavior of the baseline is selected with the strategy parameter.

All strategies make predictions that ignore the input feature values passed as the X argument to fit and predict. The predictions, however, typically depend on values observed in the y parameter passed to fit.

Note that the “stratified” and “uniform” strategies lead to non-deterministic predictions that can be rendered deterministic by setting the random\_state parameter if needed. The other strategies are naturally deterministic and, once fit, always return the same constant prediction for any value of X.

Constructors

constructor()

Signature

new DummyClassifier(opts?: object): DummyClassifier;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.constant?`	`string` \| `number` \| `ArrayLike`	The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy.
`opts.random_state?`	`number`	Controls the randomness to generate the predictions when `strategy='stratified'` or `strategy='uniform'`. Pass an int for reproducible output across multiple function calls. See Glossary.
`opts.strategy?`	`"uniform"` \| `"most_frequent"` \| `"prior"` \| `"stratified"` \| `"constant"`	Strategy to use to generate predictions. `Default Value` `'prior'`

Returns

DummyClassifier

Defined in: generated/dummy/DummyClassifier.ts:31 (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/dummy/DummyClassifier.ts:110 (opens in a new tab)

fit()

Fit the baseline classifier.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training data.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/dummy/DummyClassifier.ts:127 (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/dummy/DummyClassifier.ts:176 (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/dummy/DummyClassifier.ts:66 (opens in a new tab)

predict()

Perform classification on test vectors X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test data.

Returns

Promise<ArrayLike>

Defined in: generated/dummy/DummyClassifier.ts:211 (opens in a new tab)

predict_log_proba()

Return log probability estimates for the test vectors X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Training data.

Returns

Promise<ArrayLike[]>

Defined in: generated/dummy/DummyClassifier.ts:244 (opens in a new tab)

predict_proba()

Return probability estimates for the test vectors X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test data.

Returns

Promise<ArrayLike[]>

Defined in: generated/dummy/DummyClassifier.ts:279 (opens in a new tab)

score()

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Signature

score(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test samples. Passing `undefined` as test samples gives the same result as passing real test samples, since DummyClassifier operates independently of the sampled observations.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True labels for X.

Returns

Promise<number>

Defined in: generated/dummy/DummyClassifier.ts:314 (opens in a new tab)

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `fit`.

Returns

Promise<any>

Defined in: generated/dummy/DummyClassifier.ts:365 (opens in a new tab)

set_score_request()

Request metadata passed to the score method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `score`.

Returns

Promise<any>

Defined in: generated/dummy/DummyClassifier.ts:404 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/dummy/DummyClassifier.ts:29 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/dummy/DummyClassifier.ts:28 (opens in a new tab)

_py

PythonBridge

Defined in: generated/dummy/DummyClassifier.ts:27 (opens in a new tab)

id

string

Defined in: generated/dummy/DummyClassifier.ts:24 (opens in a new tab)

opts

any

Defined in: generated/dummy/DummyClassifier.ts:25 (opens in a new tab)

Accessors

class_prior_

Frequency of each class observed in y. For multioutput classification problems, this is computed independently for each output.

Signature

class_prior_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/dummy/DummyClassifier.ts:490 (opens in a new tab)

classes_

Unique class labels observed in y. For multi-output classification problems, this attribute is a list of arrays as each output has an independent set of possible classes.