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What a perceptron does
A perceptron is a single-layer linear classifier. Given a feature vector x, it calculates a score from the feature weights w and intercept b:
score = dot(w, x) + b
A threshold turns that score into a predicted class. In the binary implementation below, the classes are -1 and +1, and scores at or above zero map to +1. During training, the model changes its weights and intercept when it predicts an example incorrectly. Scikit-learn’s guide summarizes the behavior this way: “It updates its model only on mistakes.” (scikit-learn linear-model user guide.)
This is a perceptron, not a multilayer perceptron: it is a linear decision model, not a network of multiple learned layers.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Build a perceptron from scratch
Use this route to see each step of the learning loop. It uses NumPy for arrays and dot products; it does not use a machine-learning estimator.
- Prepare the data. Put the numeric features in a two-dimensional array
X, with one example per row, and encode each binary target inyas-1or+1. - Initialize the parameters. Start each feature weight and the intercept at zero.
- Train over the examples. For each example, calculate its score and predict a class. If the prediction differs from the target, add
learning_rate * target * xto the weights andlearning_rate * targetto the intercept. - Predict with the learned parameters. Apply the same zero threshold to scores for new rows.
import numpy as np
class Perceptron:
def __init__(self, learning_rate=1.0, epochs=20):
self.learning_rate = learning_rate
self.epochs = epochs
def fit(self, X, y):
X = np.asarray(X, dtype=float)
y = np.asarray(y, dtype=int) # labels must be -1 or +1
self.weights = np.zeros(X.shape[1])
self.bias = 0.0
for _ in range(self.epochs):
for x_i, target in zip(X, y):
prediction = 1 if np.dot(self.weights, x_i) + self.bias >= 0 else -1
if prediction != target:
self.weights += self.learning_rate * target * x_i
self.bias += self.learning_rate * target
return self
def predict(self, X):
X = np.asarray(X, dtype=float)
scores = X @ self.weights + self.bias
return np.where(scores >= 0, 1, -1)The threshold and label coding are paired: this code predicts +1 at a score of zero and expects targets in {-1, +1}. If your labels use a different convention, convert them or change the prediction and update logic consistently. The fixed epoch count is a stopping limit, not a promise that every dataset will be classified correctly.
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Use scikit-learn for a practical workflow
For a compact estimator-based workflow, create training and held-out test data, fit sklearn.linear_model.Perceptron, and evaluate predictions on the held-out examples. The following example assumes X_train, X_test, y_train, and y_test are already prepared and that the two target arrays use the same class labels.
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
model = Perceptron(max_iter=1000, tol=0.001, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(accuracy_score(y_test, predictions))The official API documents fit, predict, and score; score(X, y) returns mean accuracy on the data and labels passed to it. To assess held-out performance, pass test data rather than the training data. The linked stable API page identified itself as scikit-learn 1.9.1 on 2026-10-04; its defaults and labels may change in later versions. (Perceptron API documentation.)
In that API snapshot, defaults include fit_intercept=True, max_iter=1000, tol=0.001, and shuffle=True. Setting key training options explicitly makes the example’s intent clearer, while random_state controls randomness where applicable. The estimator is documented as equivalent to SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None). The user guide describes the default perceptron as unregularized and mistake-updated.
Choose the route that fits your goal
| Route | What you see or get | Best suited to |
|---|---|---|
| From scratch | The score, threshold, labels, and mistake update are explicit in your code. | Learning how the algorithm works or adapting a small educational example. |
| scikit-learn estimator | Standard fit, predict, and score methods, with iteration and stopping controls. |
Applying a linear classifier within a Python machine-learning workflow. |
These routes illustrate different levels of abstraction; no runtime or accuracy comparison is implied. The estimator hides the update loop, while the from-scratch version leaves the mechanics in view.
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Know what the training result means
A finite training run can stop at its epoch limit without solving the classification task. The scikit-learn estimator also exposes maximum-iteration and tolerance-based stopping settings, but neither setting should be read as a guarantee that arbitrary data will be classified perfectly. Check performance on data not used to fit the model, and remember that a perceptron remains a linear classifier.
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