Cross Validation (K-Fold)
Description
Cross Validation is a statistical method used to estimate the skill of machine learning models. It helps to assess how the results of a model will generalize to an independent dataset. K-Fold Cross Validation is one of the most commonly used techniques.
K-Fold Cross Validation
In K-Fold Cross Validation, the dataset is divided into K equally sized folds. The model is trained K times, each time using K-1 folds as training data and the remaining one fold as testing data. The final evaluation metric is the average of the results across all K trials.
- Helps reduce overfitting and gives a more reliable estimate of model performance
- Ensures every data point is used for both training and validation exactly once
- K is typically chosen as 5 or 10, balancing bias and variance
Examples
K-Fold Cross Validation Example in Python
from sklearn.datasets import load_iris
from sklearn.model_selection import KFold, cross_val_score
from sklearn.ensemble import RandomForestClassifier
import numpy as np
# Load dataset
iris = load_iris()
X, y = iris.data, iris.target
# Initialize model
model = RandomForestClassifier()
# Setup K-Fold cross-validation with 5 folds
kf = KFold(n_splits=5, shuffle=True, random_state=42)
# Evaluate model using cross-validation
scores = cross_val_score(model, X, y, cv=kf)
print("Cross-validation scores:", scores)
print("Average accuracy:", np.mean(scores))
Real-World Applications
Cross Validation Applications
- Model Selection: Comparing different models or hyperparameters to choose the best one
- Medical Diagnosis: Validating models predicting diseases to ensure reliability
- Finance: Risk modeling and fraud detection systems where robustness is critical
- Natural Language Processing: Evaluating models on limited labeled text data

Resources
The following resources will be manually added later:
Video Tutorials
PDF/DOC Materials
Interview Questions
1. What is the purpose of K-Fold Cross Validation?
K-Fold Cross Validation is used to assess how well a model generalizes to unseen data by training and testing it multiple times on different subsets of the dataset.
2. How do you choose the value of K in K-Fold Cross Validation?
K is often chosen as 5 or 10 as a trade-off between computational cost and reliable performance estimation. A smaller K increases bias but reduces variance, while a larger K reduces bias but increases variance.
3. What is the difference between K-Fold Cross Validation and simple train-test split?
Train-test split divides the data once, which can lead to variance in evaluation results. K-Fold uses multiple splits to provide a more robust and less biased estimate of model performance.
4. What is stratified K-Fold Cross Validation?
Stratified K-Fold ensures that each fold has the same proportion of class labels as the original dataset, which is important for imbalanced classification problems.