K fold validation can be used to evaluate the tools of machine learning. It is a statistical method for selecting and comparing the model used in machine learning. It makes it easy to understand and implement predictive modeling. It also helps to understand the attributes of a model. You must read this article to understand the importance of K fold validation in machine learning.
When making a machine learning model it is important to use generalization which means the algorithm’s ability to remain effective when new inputs are added. Generalization is important but it is difficult to achieve in machine learning. Therefore, it is an arduous task to check the ability of an algorithm when making a model.
For this purpose, we use cross-validation which is of different types but in this article, we will mainly focus on K fold cross-validation and its importance in machine learning.
What is K-Fold Cross-Validation?
This method helps to avoid the cons of the other method. It gives a new way of dividing the dataset. In this method, we choose a k number of folds such as 5, 10, or any other number which is less than the length of the database. Divide the data by the k number of parts. Make them equal if possible and these parts are called folds. The K-1 set will be considered as the training set and the other remaining folds are considered test sets. Use the chosen model on the training set.
Now, validate the model on the test set and save the results. Repeat the entire process k times and every time use the remaining fold as the test set. In this way, you can validate the model on all folds. You can find the average of the results to get the final score.
K-fold cross-validation has much importance in ML. This tool helps to evaluate the accuracy of the model so that it must not be underfitting or overfitting. Overfitting and underfitting are the two most important parts used in machine learning. These two things help to understand how accurately a model is built for predicting the given data. If the model is not fit, it will not help to prepare adequate data and the model will not be able to process the dataset properly.
Benefits of Using the K-fold Cross-validation Method
There are several benefits of using the K fold cross-validation method as compared to the hold-out method of cross-validation. The most important benefits of using the K fold method are:
- This method gives trustworthy and stable results because the training and tests are done on different parts of the data.
- We can improve the overall score if we use more folds for testing the model on different sub-data sets.
Stratified K-fold Cross-Validation
In some cases, the target value may vary to a great extent. For example, in a dataset containing the price of a TV set, the price of some TV sets may be very high. In such cases, the stratified K-fold method may be used which is slightly different from the standard K-fold method. The stratified K-fold method may be used in this way:
We will divide the given data into K folds in such a way that each fold will consist of the same percentage of each target category and it will be considered a complete set. The average target value in the stratified K fold is approximately the same in all folds.
The algorithm used in the stratified K-fold method is the same as that used in the standard k-fold method.
Repeated K-fold Cross-Validation Method
It is the strongest method of cross-validation used in machine learning. It is also a variation of the standard k-fold method but in this method, k is not taken as the number of folds but k is the number of times used to train the model. For example, if we consider taking 10 percent of the dataset as the test set, the rest 90 percent will be taken as the training set. The algorithm used in this method is almost the same as used in the standard k-fold method.
This method has more benefits as compared to the standard k-fold cross-validation method. The most important benefit of using this method is that the proportion of train and test split is independent of the number of iterations. This method can also give a unique proportion for each iteration. Lastly, when we select the test set randomly from the dataset this method becomes independent of the selection process.
Nested K-fold Method
This method of cross-validation is used to build a model in which optimization of hyperparameters has to be done. This method helps to find the generalization error of the model and its hyperparameters.
Tips to Cross-validate the Given Data
Some tips are given below that can help to cross-validate the given data using different methods:
Split the method properly. Ensure that you split the data in such a way that it must make sense.
- Use the best method for cross-validation
- If you are using time series avoid dividing the data avoid using validation on the past
- If you are working on financial or medical data you can divide the data using a person. Do not use data for a single person in the test and training set because it will be marked as a data leak.
- If you have to crop an image you must divide it through the larger image ID.
The tips to follow may be different for each set of given data. Therefore, it is important to perform data analysis before doing cross-validation of the given data or building a model for machine learning.
Conclusion
A cross-validation is an important tool used in machine learning. The person must choose the right cross-validation technique. A person must understand the pros and cons of different cross-validation methods used in machine learning for choosing the best method.
Selecting the right method of cross-validation will help you to build a solid model in machine learning. Cross-validation helps to compare the performance of different models used in machine learning for the given data set. This tool can also help select proper parameters.