Class: 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

new LinearSVR()

new LinearSVR(opts?): LinearSVR

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.C`?	`number`	Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.
`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`.
`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`.
`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).
`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.
`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.
`opts.max_iter`?	`number`	The maximum number of iterations to be run.
`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.
`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.

Returns LinearSVR

Defined in generated/svm/LinearSVR.ts:29

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/svm/LinearSVR.ts:27
`_isInitialized`	`boolean`	`false`	generated/svm/LinearSVR.ts:26
`_py`	`PythonBridge`	`undefined`	generated/svm/LinearSVR.ts:25
`id`	`string`	`undefined`	generated/svm/LinearSVR.ts:22
`opts`	`any`	`undefined`	generated/svm/LinearSVR.ts:23

Accessors

coef_

Get Signature

get coef_(): Promise<ArrayLike[]>

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.

Returns Promise<ArrayLike[]>

Defined in generated/svm/LinearSVR.ts:397

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/svm/LinearSVR.ts:468

intercept_

Get Signature

get intercept_(): Promise<ArrayLike>

Constants in decision function.

Returns Promise<ArrayLike>

Defined in generated/svm/LinearSVR.ts:420

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/svm/LinearSVR.ts:443

n_iter_

Get Signature

get n_iter_(): Promise<number>

Maximum number of iterations run across all classes.

Returns Promise<number>

Defined in generated/svm/LinearSVR.ts:493

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/svm/LinearSVR.ts:102

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/svm/LinearSVR.ts:153

fit()

fit(opts): Promise<any>

Fit the model according to the given training data.

Parameters

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

Returns Promise<any>

Defined in generated/svm/LinearSVR.ts:170

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

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

Returns Promise<any>

Defined in generated/svm/LinearSVR.ts:214

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/svm/LinearSVR.ts:115

predict()

predict(opts): Promise<any>

Predict using the linear model.

Parameters

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

Returns Promise<any>

Defined in generated/svm/LinearSVR.ts:248

score()

score(opts): Promise<number>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`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.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/svm/LinearSVR.ts:281

set_fit_request()

set_fit_request(opts): Promise<any>

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:

Parameters

Parameter	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:327

set_score_request()

set_score_request(opts): Promise<any>

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:

Parameters

Parameter	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:363

LinearSVC LocallyLinearEmbedding