Class: NuSVR

Nu Support Vector Regression.

Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-SVR.

The implementation is based on libsvm.

Constructors

new NuSVR()

new NuSVR(opts?): NuSVR

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.C`?	`number`	Penalty parameter C of the error term. For an intuitive visualization of the effects of scaling the regularization parameter C, see Scaling the regularization parameter for SVCs.
`opts.cache_size`?	`number`	Specify the size of the kernel cache (in MB).
`opts.coef0`?	`number`	Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
`opts.degree`?	`number`	Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels.
`opts.gamma`?	`number` \| `"auto"` \| `"scale"`	Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.
`opts.kernel`?	`"sigmoid"` \| `"precomputed"` \| `"linear"` \| `"poly"` \| `"rbf"`	Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.
`opts.max_iter`?	`number`	Hard limit on iterations within solver, or -1 for no limit.
`opts.nu`?	`number`	An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.
`opts.shrinking`?	`boolean`	Whether to use the shrinking heuristic. See the User Guide.
`opts.tol`?	`number`	Tolerance for stopping criterion.
`opts.verbose`?	`boolean`	Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

Returns NuSVR

Defined in generated/svm/NuSVR.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/svm/NuSVR.ts:25
`_isInitialized`	`boolean`	`false`	generated/svm/NuSVR.ts:24
`_py`	`PythonBridge`	`undefined`	generated/svm/NuSVR.ts:23
`id`	`string`	`undefined`	generated/svm/NuSVR.ts:20
`opts`	`any`	`undefined`	generated/svm/NuSVR.ts:21

Accessors

dual_coef_

Get Signature

get dual_coef_(): Promise<ArrayLike[]>

Coefficients of the support vector in the decision function.

Returns Promise<ArrayLike[]>

Defined in generated/svm/NuSVR.ts:403

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/NuSVR.ts:495

fit_status_

Get Signature

get fit_status_(): Promise<number>

0 if correctly fitted, 1 otherwise (will raise warning)

Returns Promise<number>

Defined in generated/svm/NuSVR.ts:426

intercept_

Get Signature

get intercept_(): Promise<ArrayLike>

Constants in decision function.

Returns Promise<ArrayLike>

Defined in generated/svm/NuSVR.ts:449

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/svm/NuSVR.ts:472

n_iter_

Get Signature

get n_iter_(): Promise<number>

Number of iterations run by the optimization routine to fit the model.

Returns Promise<number>

Defined in generated/svm/NuSVR.ts:520

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/NuSVR.ts:109

shape_fit_

Get Signature

get shape_fit_(): Promise<any[]>

Array dimensions of training vector X.

Returns Promise<any[]>

Defined in generated/svm/NuSVR.ts:542

support_

Get Signature

get support_(): Promise<ArrayLike>

Indices of support vectors.

Returns Promise<ArrayLike>

Defined in generated/svm/NuSVR.ts:565

support_vectors_

Get Signature

get support_vectors_(): Promise<ArrayLike[]>

Support vectors.

Returns Promise<ArrayLike[]>

Defined in generated/svm/NuSVR.ts:587

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/NuSVR.ts:160

fit()

fit(opts): Promise<any>

Fit the SVM model according to the given training data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
`opts.X`?	`ArrayLike`	Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
`opts.y`?	`ArrayLike`	Target values (class labels in classification, real numbers in regression).

Returns Promise<any>

Defined in generated/svm/NuSVR.ts:177

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/NuSVR.ts:221

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/NuSVR.ts:122

predict()

predict(opts): Promise<ArrayLike>

Perform regression on samples in X.

For an one-class model, +1 (inlier) or -1 (outlier) is returned.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns Promise<ArrayLike>

Defined in generated/svm/NuSVR.ts:255

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/NuSVR.ts:289

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/NuSVR.ts:335

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/NuSVR.ts:371

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