k-fold Cross Validation

K-Fold Cross Validation

Cross Validation is a statistical method that measures the skills of the Machine Learning models. You can see its application better under applied Machine Learning, where it compares and selects the model for a predictive modeling problem, and it is simpler to understand and implement. k-fold Cross Validation is one such method of Cross-Validation that estimates the skill of Machine Learning models. k-fold Cross Validation is the process through which you can estimate the performance of your Machine Learning model on the new data. You need not worry about selecting the value of k as there are definitive methods through which you can determine the value of k and divide the data accordingly.

You can find many commonly used variations on Cross Validation like the once stratified and repeated available in scikit-learn. Cross Validation is considered as the resampling procedure. It is utilized for evaluating the Machine Learning models on a limited data set. You will determine the value of the single parameter called k, which refers to the number of folds that a given data set can be divided into. This process is known as k-fold Cross Validation. You may also have a specified value of the k, which can replace k in reference to the model. For example, if the value of k is 5, it becomes 5-fold Cross-Validation. Cross Validation is primarily known for using it on unseen data to estimate the Machine Learning model skill. The k-fold Cross Validation algorithm is utilized when you want to determine its performance in general by testing it against some number. It is mainly used for making predictions and isn’t used during the model’s training. 

The k-fold Cross Validation is a popular approach as it is simple to understand and implement. It also provides a less biased or less optimistic estimate of the model skills compared to other methods. The general steps to achieve k-fold Cross Validation are:

• Randomly shuffle the data set.

• Split the datasets into k folds.

• For each of these unique folds:

  • Consider the fold or the group as a test data set.

  • Consider the remaining folds as the training data set.

  • Fit the model on the training data set and evaluate it on the test data set.

  • Retrieve the evaluation score and discard the model.

• Use a sample of model evaluation score to summarize the model’s skill.

When you follow the procedures of k-fold Cross Validation, you will know that each observation in the data set is assigned to an individual fold and stays in that fold for the duration of the procedure. This concludes that each data set is given the opportunity to be used in the test data set one time and used to train the model k-1 times. The summarization of the k-fold Cross Validation run is achieved with the help of the mean of the model skill scores. It is considered a good practice to involve a measure of the variance of the skill scores. For example, standard error or standard deviation.

The most critical task in k-fold Cross Validation is finding out the value of k as the whole method depends upon the configuration of k. As we use the k value for the division of the data set, it becomes crucial for us to choose the value wisely. If you choose a poor value for k, it may result in misinterpreting the model skills. To avoid such situations, it becomes essential for a data scientist to take special care while choosing the value of k. If you prefer some random poor value, it may give you the wrong impression of the model skill, like a high variance score or a high bias. Thus, to get rid of all these possibilities, there are some rules for determining the value of k. Three common tactics in finding the value of k include:

• Representative : The value of k is selected in such a manner where each test/train data sets are large enough to represent the broader data set statistically.

• k=10 : This value 10 of k is generally found in many experimentations, which gives low bias modest variance estimation of a data set.

• k=n : Here, the value of k is set to n, where n is the size of the data set that provides each data set an opportunity to be utilized in the test data set. This technique is known as the leave-one-out-Cross-Validation.

If you wish to know the k-fold Cross Validation method’s performance, go through k-fold Cross Validation examples. There are plenty of examples introduced in the articles, blogs, or courses regarding k-fold Cross-Validation. You can visit those pages and get familiar with the working of the algorithm. If you want to learn it in-depth, enroll in the free k-fold Cross Validation course Great Learning offers. Complete all the course modules successfully and get hold of the free k-fold Cross Validation certificate. Enroll today and build your career in Data Science.