Scikit-Learn RidgeClassifier Model

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:

1. 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 using train_test_split().

2. Next, a RidgeClassifier model is instantiated with default hyperparameters. The model is then fit on the training data using the fit() method.

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

4. 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.