por Within the method of cross validation of machine learningknow What the K folds method is is essential to finish testing the performance of a predictive model that is made with this field of artificial intelligence. So, below we present what the K folds method is, so that you can continue exploring many more concepts related to programming and, in this way, become an expert in machine learning.
What is cross-validation?
Before going on to answer the question about what the K folds method is, let’s remember where this method comes from, based on the general concept of cross-validationwell it is one of the models that comes out of there.
As we told you in the article about cross validation, this is a method that will allow you to predict how the system will work. program and what may be the breaking points or the points that can generate benefits for the program.
What is the K folds method?
As we told you a moment ago, the K folds method is a type module cross-validation, whose concept and way of working is quite simple. Therefore, it is well known within the spectrum of programming, artificial intelligence and machine learning.
The K folds method allows all approximations around data sets to be established in training sets and appear at other points in the process, such as at the training and test points. According to experts, this is very feasible to control limited input data.
Now you have to know how the K folds method works, a topic that we will explore in the next section, where we will tell you all the steps to create a program with this method.
How the K folds method works
The strategy presented by the K folds method is quite simple, so it presents many results that can benefit and help meet the objectives budgeted for a project. Therefore, to make a strategy with the K folds method, you must follow the steps we discuss below:
First of all, you must separate the series or grouping of data randomly into folders type K ok – folds. We recommend that these k folders be no more than 10 and no less than 5, depending on the entire size of the data series. Higher or lower K values can lead to problems with the model, resulting in bias or overfitting. Afterwards, the model must be adjusted with the groupings of the K folds, minus one, which remains. The latter will be the one that validates the model and the other folds, so that it records scores and errors. This process must be iterative until all K folders have been validated.
In this way, the K folds model is used to segment and train the model, to then give a result relevant to the model’s performance prediction.
Continue learning in Keepcoding
Since, thanks to this article, you now know what the K folds method is and what its importance is within the cross validation or cross-validation within the machine learningyou are now closer to becoming an expert in machine learning or ML.
Now, if you would like to explore other similar concepts to become an expert in the world of big data, we invite you to sign up for our Big Data, Artificial Intelligence & Machine Learning Full Stack Bootcamp. With this intensive training you will learn from great professionals and, in a few months, you will be prepared to enter and stand out in the IT job market. Don’t miss the opportunity and come in to ask for more information!