What is k-fold cross-validation, and how does it work?

A k-fold cross-validation is an approach using which one can estimate the performance of a machine learning model. In k-fold cross-validation, the dataset is divided into k parts called k folds. Then, the (k-1) folds are used to train the machine learning model, while the left-out fold is used to test the model. This process is repeated so that each fold is used once for testing the model.

How to implement k-fold cross-validation using sklearn?

We can use the following Python code to implement k-fold cross-validation.

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

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

k_fold = KFold(n_splits=10, shuffle=True, random_state=1)
classifier = LogisticRegression(solver="liblinear")

results = cross_val_score(classifier, X, y, cv=k_fold, scoring="accuracy")
mean_score = results.mean()
print("Accuracy: ", mean_score)

Here, we are first reading the Pima Indians Diabetes dataset. The dataset contains various predictor variables such as the …