RidgeClassifier is a linear classifier with L2 regularization, useful for both binary and multi-class classification problems. The key hyperparameters of RidgeClassifier
include alpha
(regularization strength), solver
(algorithm used in the optimization problem), and max_iter
(maximum number of iterations).
The algorithm is appropriate for binary and multi-class classification problems.
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import RidgeClassifier
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 = RidgeClassifier()
# 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:
First, a synthetic binary classification dataset is generated 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 usingtrain_test_split()
.Next, a
RidgeClassifier
model is instantiated with default hyperparameters. The model is then fit on the training data using thefit()
method.The performance of the model is evaluated by comparing the predictions (
yhat
) to the actual values (y_test
) using the accuracy score metric.A single prediction can be made by passing a new data sample to the
predict()
method.
This example demonstrates how to set up and use a RidgeClassifier
model for binary classification tasks. The model can be fit directly on the training data and used to make predictions on new data, showcasing its utility in real-world classification problems.