class sklearn.svm.OneClassSVM(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False, max_iter=- 1) [source]
 
Unsupervised Outlier Detection. Estimate the support of a high-dimensional distribution. The implementation is based on libsvm. Read more in the User Guide.  Parameters 
 
kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, default=’rbf’ 

Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.  
degreeint, default=3 

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.  
gamma{‘scale’, ‘auto’} or float, default=’scale’ 

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.  if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma, if ‘auto’, uses 1 / n_features.   Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.   
coef0float, default=0.0 

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.  
tolfloat, default=1e-3 

Tolerance for stopping criterion.  
nufloat, default=0.5 

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.  
shrinkingbool, default=True 

Whether to use the shrinking heuristic. See the User Guide.  
cache_sizefloat, default=200 

Specify the size of the kernel cache (in MB).  
verbosebool, default=False 

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.  
max_iterint, default=-1 

Hard limit on iterations within solver, or -1 for no limit.    Attributes 
 
class_weight_ndarray of shape (n_classes,) 

Multipliers of parameter C for each class. Computed based on the class_weight parameter.  
coef_ndarray of shape (1, n_features) 

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. coef_ is readonly property derived from dual_coef_ and support_vectors_.  
dual_coef_ndarray of shape (1, n_SV) 

Coefficients of the support vectors in the decision function.  
fit_status_int 

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

Constant in the decision function.  
n_support_ndarray of shape (n_classes,), dtype=int32 

Number of support vectors for each class.  
offset_float 

Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset_. The offset is the opposite of intercept_ and is provided for consistency with other outlier detection algorithms.  New in version 0.20.   
shape_fit_tuple of int of shape (n_dimensions_of_X,) 

Array dimensions of training vector X.  
support_ndarray of shape (n_SV,) 

Indices of support vectors.  
support_vectors_ndarray of shape (n_SV, n_features) 

Support vectors.     Examples >>> from sklearn.svm import OneClassSVM
>>> X = [[0], [0.44], [0.45], [0.46], [1]]
>>> clf = OneClassSVM(gamma='auto').fit(X)
>>> clf.predict(X)
array([-1,  1,  1,  1, -1])
>>> clf.score_samples(X)
array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])
 Methods  
decision_function(X) Signed distance to the separating hyperplane.  
fit(X[, y, sample_weight]) Detects the soft boundary of the set of samples X.  
fit_predict(X[, y]) Perform fit on X and returns labels for X.  
get_params([deep]) Get parameters for this estimator.  
predict(X) Perform classification on samples in X.  
score_samples(X) Raw scoring function of the samples.  
set_params(**params) Set the parameters of this estimator.    
decision_function(X) [source]
 
Signed distance to the separating hyperplane. Signed distance is positive for an inlier and negative for an outlier.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

The data matrix.    Returns 
 
decndarray of shape (n_samples,) 

Returns the decision function of the samples.     
  
fit(X, y=None, sample_weight=None, **params) [source]
 
Detects the soft boundary of the set of samples X.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Set of samples, where n_samples is the number of samples and n_features is the number of features.  
sample_weightarray-like of shape (n_samples,), default=None 

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.  
yIgnored 

not used, present for API consistency by convention.    Returns 
 
selfobject 
   Notes If X is not a C-ordered contiguous array it is copied. 
  
fit_predict(X, y=None) [source]
 
Perform fit on X and returns labels for X. Returns -1 for outliers and 1 for inliers.  Parameters 
 
X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features) 

yIgnored 

Not used, present for API consistency by convention.    Returns 
 
yndarray of shape (n_samples,) 

1 for inliers, -1 for outliers.     
  
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.     
  
predict(X) [source]
 
Perform classification on samples in X. For a one-class model, +1 or -1 is returned.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train) 

For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).    Returns 
 
y_predndarray of shape (n_samples,) 

Class labels for samples in X.     
  
score_samples(X) [source]
 
Raw scoring function of the samples.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

The data matrix.    Returns 
 
score_samplesndarray of shape (n_samples,) 

Returns the (unshifted) scoring function of the samples.     
  
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.