Class: SVC

C-Support Vector Classification.

The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using LinearSVC or SGDClassifier instead, possibly after a Nystroem transformer or other Kernel Approximation.

The multiclass support is handled according to a one-vs-one scheme.

For details on the precise mathematical formulation of the provided kernel functions and how gamma, coef0 and degree affect each other, see the corresponding section in the narrative documentation: Kernel functions.

To learn how to tune SVC’s hyperparameters, see the following example: Nested versus non-nested cross-validation

Constructors

new SVC()

new SVC(opts?): SVC

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.break_ties`?	`boolean`	If true, `decision_function_shape='ovr'`, and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.
`opts.C`?	`number`	Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty. 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.class_weight`?	`any`	Set the parameter C of class i to class_weight[i]C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes \ np.bincount(y))`.
`opts.coef0`?	`number`	Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
`opts.decision_function_shape`?	`"ovr"` \| `"ovo"`	Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, note that internally, one-vs-one (‘ovo’) is always used as a multi-class strategy to train models; an ovr matrix is only constructed from the ovo matrix. The parameter is ignored for binary classification.
`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 pre-compute the kernel matrix from data matrices; that matrix should be an array of shape `(n_samples, n_samples)`. For an intuitive visualization of different kernel types see Plot classification boundaries with different SVM Kernels.
`opts.max_iter`?	`number`	Hard limit on iterations within solver, or -1 for no limit.
`opts.probability`?	`boolean`	Whether to enable probability estimates. This must be enabled prior to calling `fit`, will slow down that method as it internally uses 5-fold cross-validation, and `predict_proba` may be inconsistent with `predict`. Read more in the User Guide.
`opts.random_state`?	`number`	Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when `probability` is `false`. Pass an int for reproducible output across multiple function calls. See Glossary.
`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 SVC

Defined in generated/svm/SVC.ts:31

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/svm/SVC.ts:29
`_isInitialized`	`boolean`	`false`	generated/svm/SVC.ts:28
`_py`	`PythonBridge`	`undefined`	generated/svm/SVC.ts:27
`id`	`string`	`undefined`	generated/svm/SVC.ts:24
`opts`	`any`	`undefined`	generated/svm/SVC.ts:25

Accessors

class_weight_

Get Signature

get class_weight_(): Promise<ArrayLike>

Multipliers of parameter C for each class. Computed based on the class_weight parameter.

Returns Promise<ArrayLike>

Defined in generated/svm/SVC.ts:530

classes_

Get Signature

get classes_(): Promise<ArrayLike>

The classes labels.

Returns Promise<ArrayLike>

Defined in generated/svm/SVC.ts:553

dual_coef_

Get Signature

get dual_coef_(): Promise<ArrayLike[]>

Dual coefficients of the support vector in the decision function (see Mathematical formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-class section of the User Guide for details.

Returns Promise<ArrayLike[]>

Defined in generated/svm/SVC.ts:575

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/SVC.ts:665

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/SVC.ts:597

intercept_

Get Signature

get intercept_(): Promise<ArrayLike>

Constants in decision function.

Returns Promise<ArrayLike>

Defined in generated/svm/SVC.ts:620

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/svm/SVC.ts:642

n_iter_

Get Signature

get n_iter_(): Promise<ArrayLike>

Number of iterations run by the optimization routine to fit the model. The shape of this attribute depends on the number of models optimized which in turn depends on the number of classes.

Returns Promise<ArrayLike>

Defined in generated/svm/SVC.ts:688

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/SVC.ts:137

shape_fit_

Get Signature

get shape_fit_(): Promise<any[]>

Array dimensions of training vector X.

Returns Promise<any[]>

Defined in generated/svm/SVC.ts:755

support_

Get Signature

get support_(): Promise<ArrayLike>

Indices of support vectors.

Returns Promise<ArrayLike>

Defined in generated/svm/SVC.ts:710

support_vectors_

Get Signature

get support_vectors_(): Promise<ArrayLike[]>

Support vectors. An empty array if kernel is precomputed.

Returns Promise<ArrayLike[]>

Defined in generated/svm/SVC.ts:732

Methods

decision_function()

decision_function(opts): Promise<ArrayLike[]>

Evaluate the decision function for the samples in X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The input samples.

Returns Promise<ArrayLike[]>

Defined in generated/svm/SVC.ts:205

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/SVC.ts:188

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/SVC.ts:237

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/SVC.ts:280

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/SVC.ts:150

predict()

predict(opts): Promise<ArrayLike>

Perform classification on samples in X.

For an one-class model, +1 or -1 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/SVC.ts:314

predict_log_proba()

predict_log_proba(opts): Promise<ArrayLike[]>

Compute log probabilities of possible outcomes for samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to true.

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/SVC.ts:348

predict_proba()

predict_proba(opts): Promise<ArrayLike[]>

Compute probabilities of possible outcomes for samples in X.

The model needs to have probability information computed at training time: fit with attribute probability set to true.

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/SVC.ts:382

score()

score(opts): Promise<number>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples.
`opts.y`?	`ArrayLike`	True labels for `X`.

Returns Promise<number>

Defined in generated/svm/SVC.ts:416

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/SVC.ts:462

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/SVC.ts:498

Sum SVR