Scikit-Learn Perceptron Model

Perceptron is a simple linear binary classifier effective for linearly separable data.

The key hyperparameters of Perceptron include penalty (regularization term), alpha (constant that multiplies the regularization term), max_iter (maximum number of iterations), and tol (stopping criterion).

The algorithm is appropriate for binary classification problems.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score

# generate binary classification dataset
X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=1)

# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# create model
model = Perceptron()

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
acc = accuracy_score(y_test, yhat)
print('Accuracy: %.3f' % acc)

# make a prediction
row = [[-1.10325445, -0.49821356, -0.05962247, -0.89224592, -0.70158632]]
yhat = model.predict(row)
print('Predicted: %d' % yhat[0])

Running the example gives an output like:

Accuracy: 0.950
Predicted: 0

The steps are as follows:

1. Generate a synthetic binary classification dataset using the make_classification() function. This creates a dataset with a specified number of samples (n_samples), features (n_features), classes (n_classes), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().

2. Create a Perceptron model with default hyperparameters. The model is then fit on the training data using the fit() method.

3. Evaluate the performance of the model by comparing the predictions (yhat) to the actual values (y_test) using the accuracy score metric.

4. Make a single prediction by passing a new data sample to the predict() method.

This example demonstrates how to quickly set up and use a Perceptron model for binary classification tasks, showcasing the simplicity and effectiveness of this algorithm in scikit-learn.

The model can be fit directly on the training data without the need for scaling or normalization. Once fit, the model can be used to make predictions on new data, enabling its use in real-world binary classification problems.