class sklearn.linear_model.Perceptron(*, penalty=None, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, eta0=1.0, n_jobs=None, random_state=0, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False) [source]
 
Read more in the User Guide.  Parameters 
 
penalty{‘l2’,’l1’,’elasticnet’}, default=None 

The penalty (aka regularization term) to be used.  
alphafloat, default=0.0001 

Constant that multiplies the regularization term if regularization is used.  
l1_ratiofloat, default=0.15 

The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty='elasticnet'.  New in version 0.24.   
fit_interceptbool, default=True 

Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.  
max_iterint, default=1000 

The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.  New in version 0.19.   
tolfloat, default=1e-3 

The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol).  New in version 0.19.   
shufflebool, default=True 

Whether or not the training data should be shuffled after each epoch.  
verboseint, default=0 

The verbosity level  
eta0double, default=1 

Constant by which the updates are multiplied.  
n_jobsint, default=None 

The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.  
random_stateint, RandomState instance, default=None 

Used to shuffle the training data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.  
early_stoppingbool, default=False 

Whether to use early stopping to terminate training when validation. score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs.  New in version 0.20.   
validation_fractionfloat, default=0.1 

The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.  New in version 0.20.   
n_iter_no_changeint, default=5 

Number of iterations with no improvement to wait before early stopping.  New in version 0.20.   
class_weightdict, {class_label: weight} or “balanced”, default=None 

Preset for the class_weight fit parameter. Weights associated with classes. 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))  
warm_startbool, default=False 

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.    Attributes 
 
classes_ndarray of shape (n_classes,) 

The unique classes labels.  
coef_ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features) 

Weights assigned to the features.  
intercept_ndarray of shape (1,) if n_classes == 2 else (n_classes,) 

Constants in decision function.  
loss_function_concrete LossFunction 

The function that determines the loss, or difference between the output of the algorithm and the target values.  
n_iter_int 

The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit.  
t_int 

Number of weight updates performed during training. Same as (n_iter_ * n_samples).      See also  
SGDClassifier

  Notes Perceptron is a classification algorithm which shares the same underlying implementation with SGDClassifier. In fact, Perceptron() is equivalent to SGDClassifier(loss="perceptron",
eta0=1, learning_rate="constant", penalty=None). References https://en.wikipedia.org/wiki/Perceptron and references therein. Examples >>> from sklearn.datasets import load_digits
>>> from sklearn.linear_model import Perceptron
>>> X, y = load_digits(return_X_y=True)
>>> clf = Perceptron(tol=1e-3, random_state=0)
>>> clf.fit(X, y)
Perceptron()
>>> clf.score(X, y)
0.939...
 Methods  
decision_function(X) Predict confidence scores for samples.  
densify() Convert coefficient matrix to dense array format.  
fit(X, y[, coef_init, intercept_init, …]) Fit linear model with Stochastic Gradient Descent.  
get_params([deep]) Get parameters for this estimator.  
partial_fit(X, y[, classes, sample_weight]) Perform one epoch of stochastic gradient descent on given samples.  
predict(X) Predict class labels for samples in X.  
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.  
set_params(**kwargs) Set and validate the parameters of estimator.  
sparsify() Convert coefficient matrix to sparse format.    
decision_function(X) [source]
 
Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.  Parameters 
 
Xarray-like or sparse matrix, shape (n_samples, n_features) 

Samples.    Returns 
 array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)

Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.     
  
densify() [source]
 
Convert coefficient matrix to dense array format. Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.  Returns 
 self

Fitted estimator.     
  
fit(X, y, coef_init=None, intercept_init=None, sample_weight=None) [source]
 
Fit linear model with Stochastic Gradient Descent.  Parameters 
 
X{array-like, sparse matrix}, shape (n_samples, n_features) 

Training data.  
yndarray of shape (n_samples,) 

Target values.  
coef_initndarray of shape (n_classes, n_features), default=None 

The initial coefficients to warm-start the optimization.  
intercept_initndarray of shape (n_classes,), default=None 

The initial intercept to warm-start the optimization.  
sample_weightarray-like, shape (n_samples,), default=None 

Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.    Returns 
 self :

Returns an instance of self.     
  
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.     
  
partial_fit(X, y, classes=None, sample_weight=None) [source]
 
Perform one epoch of stochastic gradient descent on given samples. Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.  Parameters 
 
X{array-like, sparse matrix}, shape (n_samples, n_features) 

Subset of the training data.  
yndarray of shape (n_samples,) 

Subset of the target values.  
classesndarray of shape (n_classes,), default=None 

Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.  
sample_weightarray-like, shape (n_samples,), default=None 

Weights applied to individual samples. If not provided, uniform weights are assumed.    Returns 
 self :

Returns an instance of self.     
  
predict(X) [source]
 
Predict class labels for samples in X.  Parameters 
 
Xarray-like or sparse matrix, shape (n_samples, n_features) 

Samples.    Returns 
 
Carray, shape [n_samples] 

Predicted class label per sample.     
  
score(X, y, sample_weight=None) [source]
 
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 
 
Xarray-like of shape (n_samples, n_features) 

Test samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True labels for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

Mean accuracy of self.predict(X) wrt. y.     
  
set_params(**kwargs) [source]
 
Set and validate the parameters of estimator.  Parameters 
 
**kwargsdict 

Estimator parameters.    Returns 
 
selfobject 

Estimator instance.     
  
sparsify() [source]
 
Convert coefficient matrix to sparse format. Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation. The intercept_ member is not converted.  Returns 
 self

Fitted estimator.     Notes For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits. After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.