What is a Ridge classifier?

A Ridge classifier is a classifier that uses Ridge regression to solve a classification problem. For example, let’s say there is a binary classification problem where the target variable can take two values. In the Ridge classifier, the target variable, in that case, is converted into -1 and +1. Then, the classification problem is treated like a regression task. Using the Ridge regressor, the problem is solved. If the outcome is positive, then the predicted class is +1. And if the outcome is negative, the predicted class is -1.

Ridge Classifier using sklearn in Python

We can use the following Python code to implement the Ridge classifier using sklearn in Python.

from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
import pandas

dataset = pandas.read_csv("diabetes.csv")
D = dataset.values
X = D[:, :-1]
y = D[:, -1]

model = RidgeClassifier()
kfold = KFold(n_splits=10, shuffle=True, random_state=1)
scores = cross_val_score(model, X, y, cv=kfold, scoring="accuracy")

print("Accuracy: ", scores.mean())

Here, we are first reading the Pima Indians Diabetes dataset. The dataset contains some features based on which a machine learning model can predict whether a patient is diabetic. The last column of the dataset contains the target variable. So, X here contains all the features, and y contains the target variable. …