Cross validation is a crucial technique in machine learning that helps evaluate the performance of a model on unseen data. Without cross validation, we risk overfitting the model to the training data. As a result, the model's performance on the test set will be significantly lower than its performance on the training data. This can lead to misleading conclusions about the model's effectiveness and can have negative impacts for the organizations.

There are 4 common cross validation techniques in machine learning. What are they?

Check out our Data Science training programs like Certified Artificial Intelligence Practitioner (CAIP), Certified Data Science Practitioner (CDSP), and many more!

4 Cross Validation Techniques in Machine Learning

Cross validation involves dividing the training data into multiple subsets, training the model on each subset, and testing it on the remaining subsets. There are 4 common cross validation techniques in machine learning.

1. Holdout Method

The holdout method assesses the performance of a machine learning model, involving splitting the dataset into two subsets: a training set used to train the model and a separate holdout set used to evaluate its performance. Typically, a common split ratio is 80/20 or 70/30, with the majority of the data used for training.

2. K-Fold Cross Validation

The k-fold cross validation technique divides the dataset into K folds and iteratively trains the model K times, using different folds as the validation set each time while the remaining folds form the training set. The model's performance is averaged over the K iterations, providing a more reliable estimate of its generalization performance.

3. Stratified K-Fold Cross Validation

This technique extends k-fold cross-validation technique by preserving the class distribution in each fold. This is particularly useful when dealing with imbalanced datasets, ensuring that each fold maintains a representative balance of the different classes, thereby producing more reliable and unbiased performance estimates.

4. Leave-P-Out Cross Validation 

This technique is where P samples are left out as the validation set in each iteration. This method can be expensive as it considers all possible ways to leave out P samples, but it provides a thorough assessment by exploring various combinations of training and validation sets.

How to Ensure Your Cross Validation Works

As cross validation is a crucial step in machine learning, here are several key considerations to ensure that your cross validation works well!

✓ Randomization of Data Split

Randomly shuffle the data to ensure that the distribution of classes or patterns is uniform to prevent any potential biases.

✓ Stratified Sampling

Ensure that each fold maintains the same class distribution as the original dataset, especially when dealing with imbalanced datasets.

✓ Choosing the Right Number of Folds

A smaller value speeds up the process but may lead to higher variance. Larger values reduce variance but increase the cost.

✓ Handling Time Series Data

For time series data, maintain temporal order when splitting the data. Avoid training on future data to simulate a real-world scenario.

✓ Check for Data Leakage

Ensure that no information from the test set leaks into the training set. Avoid using information from the test set to scale the training set.

✓ Performance Metrics

Choose appropriate evaluation metrics. Select metrics that align with the goals and challenges of your machine learning task.

✓ Record Keeping

Keep detailed records of the hyperparameters and any other relevant information for each fold to help in comparing models and understanding potential sources of variation.

To Sum Up...

Applying cross validation technique can help organizations in obtaining a more accurate and reliable evaluation of a model's performance. By evaluating the model, it can identify models that are likely to generalize well, and thus the organizations can create more informed and better decisions. In addition, implement the best practices to ensure that your machine learning process works as well as you expected!

References

Arya, N. (2023). Cross Validation in Machine Learning.

https://www.ejable.com/tech-corner/ai-machine-learning-and-deep-learning/cross-validation-in-machine-learning/#Cross-Validation_Process

Great Learning Team. (2022). [BLOG] What is Cross Validation and its types in Machine learning? Great Learning.

https://www.mygreatlearning.com/blog/cross-validation/

Kili Technology. (n.d.) Types of Cross Validation Techniques used in Machine Learning.

https://kili-technology.com/data-labeling/machine-learning/cross-validation-in-machine-learning

Lyashenko, V. (2023). Cross-Validation in Machine Learning: How to do it right. neptune.ai.

https://neptune.ai/blog/cross-validation-in-machine-learning-how-to-do-it-right