Conclusion

 

Cross-validation is a very powerful tool. It helps us use our data more efficiently, and it gives us better information about our algorithm’s performance. Cross-validation is primarily used in scenarios where prediction is the main goal, and the user wants to estimate how well their model will perform in real-world situations.

Cross-validation requires partitioning data into training and testing sets. This partitioning method can be varied – randomly splitting the available data set into two parts, divide the data set into slices or “folds” or simply reserve one data point for each iteration to test. The decision as to which cross-validation technique should be implemented is made based on the size of the data set and computational costs.