class sklearn.kernel_approximation.Nystroem(kernel='rbf', *, gamma=None, coef0=None, degree=None, kernel_params=None, n_components=100, random_state=None, n_jobs=None) [source]
 
Approximate a kernel map using a subset of the training data. Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis. Read more in the User Guide.  New in version 0.13.   Parameters 
 
kernelstring or callable, default=’rbf’ 

Kernel map to be approximated. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.  
gammafloat, default=None 

Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.  
coef0float, default=None 

Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.  
degreefloat, default=None 

Degree of the polynomial kernel. Ignored by other kernels.  
kernel_paramsdict, default=None 

Additional parameters (keyword arguments) for kernel function passed as callable object.  
n_componentsint, default=100 

Number of features to construct. How many data points will be used to construct the mapping.  
random_stateint, RandomState instance or None, default=None 

Pseudo-random number generator to control the uniform sampling without replacement of n_components of the training data to construct the basis kernel. Pass an int for reproducible output across multiple function calls. See Glossary.  
n_jobsint, default=None 

The number of jobs to use for the computation. This works by breaking down the kernel matrix into n_jobs even slices and computing them in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.  New in version 0.24.     Attributes 
 
components_ndarray of shape (n_components, n_features) 

Subset of training points used to construct the feature map.  
component_indices_ndarray of shape (n_components) 

Indices of components_ in the training set.  
normalization_ndarray of shape (n_components, n_components) 

Normalization matrix needed for embedding. Square root of the kernel matrix on components_.      See also  
RBFSampler


An approximation to the RBF kernel using random Fourier features.  
sklearn.metrics.pairwise.kernel_metrics


List of built-in kernels.    References  Williams, C.K.I. and Seeger, M. “Using the Nystroem method to speed up kernel machines”, Advances in neural information processing systems 2001 T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou “Nystroem Method vs Random Fourier Features: A Theoretical and Empirical Comparison”, Advances in Neural Information Processing Systems 2012  Examples >>> from sklearn import datasets, svm
>>> from sklearn.kernel_approximation import Nystroem
>>> X, y = datasets.load_digits(n_class=9, return_X_y=True)
>>> data = X / 16.
>>> clf = svm.LinearSVC()
>>> feature_map_nystroem = Nystroem(gamma=.2,
...                                 random_state=1,
...                                 n_components=300)
>>> data_transformed = feature_map_nystroem.fit_transform(data)
>>> clf.fit(data_transformed, y)
LinearSVC()
>>> clf.score(data_transformed, y)
0.9987...
 Methods  
fit(X[, y]) Fit estimator to data.  
fit_transform(X[, y]) Fit to data, then transform it.  
get_params([deep]) Get parameters for this estimator.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Apply feature map to X.    
fit(X, y=None) [source]
 
Fit estimator to data. Samples a subset of training points, computes kernel on these and computes normalization matrix.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data.     
  
fit_transform(X, y=None, **fit_params) [source]
 
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Input samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None 

Target values (None for unsupervised transformations).  
**fit_paramsdict 

Additional fit parameters.    Returns 
 
X_newndarray array of shape (n_samples, n_features_new) 

Transformed array.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Apply feature map to X. Computes an approximate feature map using the kernel between some training points and X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Data to transform.    Returns 
 
X_transformedndarray of shape (n_samples, n_components) 

Transformed data.