Comparison between cross-validation and hold out method
Advantages of train/test split:
- This runs K times faster than Leave One Out cross-validation because K-fold cross-validation repeats the train/test split K-times.
- Simpler to examine the detailed results of the testing process.
Advantages of cross-validation:
- More accurate estimate of out-of-sample accuracy.
- More “efficient” use of data as every observation is used for both training and testing.
Cross Validation in Machine Learning
In machine learning, we couldn’t fit the model on the training data and can’t say that the model will work accurately for the real data. For this, we must assure that our model got the correct patterns from the data, and it is not getting up too much noise. For this purpose, we use the cross-validation technique. In this article, we’ll delve into the process of cross-validation in machine learning.