LinearSVR

Linear Support Vector Regression.

Similar to SVR with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

The main differences between LinearSVR and SVR lie in the loss function used by default, and in the handling of intercept regularization between those two implementations.

This class supports both dense and sparse input.

Constructors

constructor()

Signature

new LinearSVR(opts?: object): LinearSVR;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.C?`	`number`	Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. `Default Value` `1`
`opts.dual?`	`boolean` \| `"auto"`	Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=`false` when n_samples > n_features. `dual="auto"` will choose the value of the parameter automatically, based on the values of `n\_samples`, `n\_features` and `loss`. If `n\_samples` < `n\_features` and optimizer supports chosen `loss`, then dual will be set to `true`, otherwise it will be set to `false`. `Default Value` `true`
`opts.epsilon?`	`number`	Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set `epsilon=0`. `Default Value` `0`
`opts.fit_intercept?`	`boolean`	Whether or not to fit an intercept. If set to `true`, the feature vector is extended to include an intercept term: `\[x\_1, ..., x\_n, 1\]`, where 1 corresponds to the intercept. If set to `false`, no intercept will be used in calculations (i.e. data is expected to be already centered). `Default Value` `true`
`opts.intercept_scaling?`	`number`	When `fit\_intercept` is `true`, the instance vector x becomes `\[x\_1, ..., x\_n, intercept\_scaling\]`, i.e. a “synthetic” feature with a constant value equal to `intercept\_scaling` is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight. Note that liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the `intercept\_scaling` parameter can be set to a value greater than 1; the higher the value of `intercept\_scaling`, the lower the impact of regularization on it. Then, the weights become `\[w\_x\_1, ..., w\_x\_n, w\_intercept\*intercept\_scaling\]`, where `w\_x\_1, ..., w\_x\_n` represent the feature weights and the intercept weight is scaled by `intercept\_scaling`. This scaling allows the intercept term to have a different regularization behavior compared to the other features. `Default Value` `1`
`opts.loss?`	`"epsilon_insensitive"` \| `"squared_epsilon_insensitive"`	Specifies the loss function. The epsilon-insensitive loss (standard SVR) is the L1 loss, while the squared epsilon-insensitive loss (‘squared_epsilon_insensitive’) is the L2 loss. `Default Value` `'epsilon_insensitive'`
`opts.max_iter?`	`number`	The maximum number of iterations to be run. `Default Value` `1000`
`opts.random_state?`	`number`	Controls the pseudo random number generation for shuffling the data. Pass an int for reproducible output across multiple function calls. See Glossary.
`opts.tol?`	`number`	Tolerance for stopping criteria. `Default Value` `0.0001`
`opts.verbose?`	`number`	Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context. `Default Value` `0`

Returns

LinearSVR

Defined in: generated/svm/LinearSVR.ts:29

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/svm/LinearSVR.ts:164

fit()

Fit the model according to the given training data.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Training vector, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.sample_weight?`	`ArrayLike`	Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
`opts.y?`	`ArrayLike`	Target vector relative to X.

Returns

Promise<any>

Defined in: generated/svm/LinearSVR.ts:181

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/svm/LinearSVR.ts:230

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/svm/LinearSVR.ts:115

predict()

Predict using the linear model.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Samples.

Returns

Promise<any>

Defined in: generated/svm/LinearSVR.ts:265

score()

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n\_samples, n\_samples\_fitted)`, where `n\_samples\_fitted` is the number of samples used in the fitting for the estimator.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True values for `X`.

Returns

Promise<number>

Defined in: generated/svm/LinearSVR.ts:298

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/svm/LinearSVR.ts:349

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/svm/LinearSVR.ts:386

Properties

_isDisposed

boolean = false

Defined in: generated/svm/LinearSVR.ts:27

_isInitialized

boolean = false

Defined in: generated/svm/LinearSVR.ts:26

_py

PythonBridge

Defined in: generated/svm/LinearSVR.ts:25

id

string

Defined in: generated/svm/LinearSVR.ts:22

opts

any

Defined in: generated/svm/LinearSVR.ts:23

Accessors

coef_

Weights assigned to the features (coefficients in the primal problem).

coef\_ is a readonly property derived from raw\_coef\_ that follows the internal memory layout of liblinear.

Signature

coef_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/svm/LinearSVR.ts:421

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/svm/LinearSVR.ts:492

intercept_

Constants in decision function.

Signature

intercept_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/LinearSVR.ts:444

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/LinearSVR.ts:467

n_iter_

Maximum number of iterations run across all classes.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/LinearSVR.ts:517

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/svm/LinearSVR.ts:102

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/svm/LinearSVR.ts:106

Files

LinearSVR.md

Latest commit

History

LinearSVR.md

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LinearSVR

Constructors

constructor()

Signature

Parameters

Returns

Methods

dispose()

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Returns

fit()

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Parameters

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get_metadata_routing()

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Parameters

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init()

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Parameters

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predict()

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Parameters

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score()

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set_fit_request()

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Parameters

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set_score_request()

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Properties

_isDisposed

_isInitialized

_py

id

opts

Accessors

coef_

Signature

Returns

feature_names_in_

Signature

Returns

intercept_

Signature

Returns

n_features_in_

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Returns

n_iter_

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Returns

py

Signature

Returns

Signature

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Returns