Implementation:Scikit learn Scikit learn Perceptron

Knowledge Sources	Scikit_learn Scikit-learn Docs
Domains	Machine Learning, Classification
Last Updated	2026-02-08 15:00 GMT

Overview

Concrete tool for performing linear classification using the perceptron algorithm, provided by scikit-learn.

Description

The Perceptron class implements a linear perceptron classifier. It is a wrapper around SGDClassifier with the loss fixed to "perceptron" and the learning rate set to "constant". It supports L2, L1, and Elastic Net regularization, partial fitting for online learning, and class weighting for imbalanced datasets.

Usage

Use this classifier for simple, fast linear classification tasks, especially when you need an online learning algorithm or a baseline linear classifier. It is particularly suitable for large-scale sparse datasets.

Code Reference

Source Location

• Repository: scikit-learn
• File: sklearn/linear_model/_perceptron.py

Signature

class Perceptron(BaseSGDClassifier):
    def __init__(
        self,
        *,
        penalty=None,
        alpha=0.0001,
        l1_ratio=0.15,
        fit_intercept=True,
        max_iter=1000,
        tol=1e-3,
        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,
    ):

Import

from sklearn.linear_model import Perceptron

I/O Contract

Inputs

Name	Type	Required	Description
penalty	str or None	No	Regularization term: 'l2', 'l1', 'elasticnet', or None (default None)
alpha	float	No	Regularization constant (default 0.0001)
l1_ratio	float	No	Elastic Net mixing parameter (default 0.15)
fit_intercept	bool	No	Whether to fit the intercept (default True)
max_iter	int	No	Maximum number of passes over training data (default 1000)
tol	float or None	No	Stopping criterion tolerance (default 1e-3)
shuffle	bool	No	Whether to shuffle training data each epoch (default True)
random_state	int, RandomState or None	No	Random seed (default 0)

Outputs

Name	Type	Description
coef_	ndarray of shape (1, n_features) or (n_classes, n_features)	Weight vectors
intercept_	ndarray of shape (1,) or (n_classes,)	Intercept (bias) terms
n_iter_	int	Actual number of iterations to reach convergence

Usage Examples

Basic Usage

from sklearn.linear_model import Perceptron
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=100, random_state=0)
clf = Perceptron(random_state=0)
clf.fit(X, y)
print(clf.score(X, y))
print(clf.predict([[0.5] * 20]))

Related Pages

• Principle:Scikit_learn_Scikit_learn_Linear_Regression