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k-fold Cross Validation
Learn to elevate algorithm performance efficiency with K-Fold Cross-Validation approach.
Instructor:
Mr. Anirudh RaoModules updated 07/2025
k-fold Cross Validation
1.8K+ learners enrolled so far
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Skills you will gain
Introduction to Cross Validation
Process of Cross Validation
Types of Cross Validation
Key Highlights

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About this course
Machine learning is a fascinating process that is considered a foundational concept for the future. But to understand how machine learning truly works, you have to understand how the data is fed into the algorithm to get it to train on the data and later test it. On the whole, it seems like a simple process but just feeding data into the algorithm in any fashion and expect it to work at good efficiency will not always work. Cross-validation is an extremely important concept that has the capability to elevate a decent algorithm into performing extremely well. Since it is this critical for you to know, we here at Great Learning have come up with this course on k fold cross-validation to help you understand it completely. A good amount of theory and hands-on sessions are discussed to help you understand all of this in a very easy-to-understand manner.
Course outline
Overview of Machine Learning
In this module, you shall understand what machine learning is, why it is needed, supervised learning, its applications, unsupervised learning, and its applications.
Introduction to Cross Validation
Process of Cross Validation
Types of Cross Validation
Understanding k-fold Cross Validation
Stratified k-fold Cross Validation
k-fold Demo with Python
Get access to the complete curriculum once you enroll in the course
Stand out with an industry-recognized certificate

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k-fold Cross Validation
4.61
1.5 Hours
Intermediate
1.8K+ learners enrolled so far

Get free course content

Master in-demand skills & tools

Test your skills with quizzes
Our course instructor

Mr. Anirudh Rao
Data Science Expert


Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
What is k-fold Cross Validation?
It is one of the most popular data partitioning strategies used by data scientists to use the dataset to create a generalized model effectively. It helps them get more accurate results.
How do you select K in k-fold Cross Validation?
Split the entire data set into K folds where the value of K shouldn’t be too high or too low. Usually, it is chosen between five to ten depending on the available data set range.
What is the difference between k-fold and Cross Validation?
Many other Cross-Validation techniques involve dividing the datasets based on some specified rules. But k-fold Cross Validation involves dividing the data set into k folds that are approximately equal in size. These folds are tested and trained for k times, and each time the different folds of data points are used for validation.
Why do we use k-fold Cross Validation?
There are some specified rules in many of the Machine Learning techniques to group the data sets. In the case of k-fold Cross Validation, we divide the data sets with some k value, and all these k-folds are of approximately similar size. It ensures that every observation from the original dataset appears in the training and test sets.
Will I get a certificate after completing this K-Fold Cross Validation free course?
Yes, you will get a certificate of completion for K-Fold Cross Validation after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
How much does this K-Fold Cross Validation course cost?
It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of K-Fold Cross Validation can get started with this course.
Is there any limit on how many times I can take this free course?
Once you enroll in the K-Fold Cross Validation course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
Can I sign up for multiple courses from Great Learning Academy at the same time?
Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.
Why choose Great Learning Academy for this free K-Fold Cross Validation course?
Great Learning Academy provides this K-Fold Cross Validation course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
What are the steps to enroll in this K-Fold Cross Validation course?
Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.
Will I have lifetime access to this free K-Fold Cross Validation course?
Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to.
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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:
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Randomly shuffle the data set.
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Split the datasets into k folds.
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For each of these unique folds:
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Consider the fold or the group as a test data set.
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Consider the remaining folds as the training data set.
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Fit the model on the training data set and evaluate it on the test data set.
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Retrieve the evaluation score and discard the model.
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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:
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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.
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k=10 : This value 10 of k is generally found in many experimentations, which gives low bias modest variance estimation of a data set.
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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.