lale.lib.autogen.nu_svr module

class lale.lib.autogen.nu_svr.NuSVR(*, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • nu (float, not for optimizer, default 0.5) – An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors

  • C (float, not for optimizer, default 1.0) – Penalty parameter C of the error term.

  • kernel (‘linear’, ‘poly’, ‘precomputed’, ‘sigmoid’, or ‘rbf’, default ‘rbf’) – Specifies the kernel type to be used in the algorithm

  • degree (integer, >=2 for optimizer, <=3 for optimizer, uniform distribution, default 3) – Degree of the polynomial kernel function (‘poly’)

  • gamma (union type, default 'scale') –

    Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’

    • float, not for optimizer

    • or ‘scale’ or ‘auto’

  • coef0 (float, not for optimizer, default 0.0) – Independent term in kernel function

  • shrinking (boolean, default True) – Whether to use the shrinking heuristic.

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.001) – Tolerance for stopping criterion.

  • cache_size (float, >=0.0 for optimizer, <=1.0 for optimizer, uniform distribution, default 200) – Specify the size of the kernel cache (in MB).

  • verbose (boolean, not for optimizer, default False) – Enable verbose output

  • max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default -1) – Hard limit on iterations within solver, or -1 for no limit.

Notes

constraint-1 : any type

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of features

  • y (array of items : float) – Target values (class labels in classification, real numbers in regression)

  • sample_weight (array, optional of items : float) – Per-sample weights

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) – For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns

result – Perform regression on samples in X.

Return type

array of items : float