Services
Technology Web App Development Mobile App Development Windows App Development Database Development UI/UX Development Cloud Development SEO Digital Marketing Guest Posting Content Writing Artificial Intelligence Data Science ERP Development CRM Development CMS Development LMS Development Business Listing Blockchain Technology Startup Promotion
Products
Data Converter Cleaner TUC - Unit Converter Import Export Survey Manager Tweet Controller
Developer
Article Blog Forum Interview Beginner News Startup Quiz
Users
Career
Apply Job Internship
Pricing
Login Sign Up Get a Quote

interview

home / developersection / interviews / what is cross-validation?
Ask Question
Anubhav Sharma
Anubhav Sharma

Student

The Anubhav portal was launched in March 2015 at the behest of the Hon'ble Prime Minister for retiring government officials to leave a record of their experiences while in Govt service .

Message

1 Answers

Anubhav Sharma
Anubhav Sharma 05 Mar 2026
Report Abuse

What is Cross-Validation?

Cross-Validation is a technique used in Machine Learning to check how well a model will perform on new, unseen data.

In simple terms, it helps answer an important question:

“Is my model really good, or did it just memorize the training data?”

This is important because many models suffer from Overfitting, where they perform very well on training data but fail on new data.

Cross-validation helps prevent this problem.

Simple Idea (Layman Example)

Imagine a teacher wants to test students.

Instead of asking questions only from one chapter, the teacher randomly selects questions from different chapters to see if students truly understand the subject.

Cross-validation does the same thing with data.

It repeatedly splits the dataset into training and testing parts to evaluate the model more reliably.

How Cross-Validation Works

The dataset is divided into multiple parts called folds.

The model is trained on some folds and tested on the remaining fold.

This process repeats several times.

Example with 5 folds:

Dataset → Split into 5 parts

Fold 1 → Test
Fold 2 → Train
Fold 3 → Train
Fold 4 → Train
Fold 5 → Train

Next round:

Fold 1 → Train
Fold 2 → Test
Fold 3 → Train
Fold 4 → Train
Fold 5 → Train

This continues until every fold has been used as a test set once.

Finally, the performance scores are averaged.

K-Fold Cross-Validation

The most common type is K‑Fold Cross‑Validation.

Steps:

  • Split the dataset into K equal parts
  • Train the model K times
  • Each time, use a different fold as the test set
  • Calculate the average performance

Example:

If K = 5

Training/Test runs = 5

This gives a more stable and reliable model evaluation.

Example

Suppose you build a Decision Tree model.

Dataset size:

1000 rows

Using 5-Fold Cross-Validation:

Fold size = 200 rows

Process:

Round Training Data Testing Data
1 800 rows 200 rows
2 800 rows 200 rows
3 800 rows 200 rows
4 800 rows 200 rows
5 800 rows 200 rows

Average accuracy from all rounds gives the final model performance.

Types of Cross-Validation

1. K-Fold Cross-Validation

Most commonly used technique.

2. Stratified K-Fold

Used for classification problems where class distribution must stay balanced.

3. Leave-One-Out Cross-Validation (LOOCV)

Here:

Training = All data except 1 row
Testing = That 1 row

This repeats for every row.

Real-World Usage

Cross-validation is used in many machine learning tasks:

  • Spam detection
  • Fraud detection
  • Medical diagnosis
  • Recommendation systems

It is also commonly used with algorithms like Decision Tree, Random Forest, and Support Vector Machine.

One-Line Summary

Cross-Validation is a method to test how well a machine learning model will perform on unseen data by repeatedly training and testing it on different parts of the dataset.           

advertising