Limitations of Cross Validation and Applications of CV

Data Science, AI and ML
#1

Limitations of Cross-Validation

There are some limitations of the cross-validation technique, which are given below:

  • For the ideal conditions, it provides the optimum output. But for the inconsistent data, it may produce a drastic result. So, it is one of the big disadvantages of cross-validation, as there is no certainty of the type of data in machine learning.
  • In predictive modeling, the data evolves over a period, due to which, it may face the differences between the training set and validation sets. Such as if we create a model for the prediction of stock market values, and the data is trained on the previous 5 years stock values, but the realistic future values for the next 5 years may drastically different, so it is difficult to expect the correct output for such situations.

Applications of Cross-Validation

  • This technique can be used to compare the performance of different predictive modeling methods.
  • It has great scope in the medical research field.
  • It can also be used for the meta-analysis, as it is already being used by the data scientists in the field of medical statistics.