Visualizing 3 Sklearn Cross-validation: K-Fold, Shuffle & Split, and Time Series Split





Basically, cross-validation is a statistical method for evaluating learning algorithms. A fixed number of folds (groups of data) is set to run the analysis. These folds group the data into 2 sets: training and testing (validation) sets, that are cross-over in rounds, allowing each data point to be validated.

The main purpose is to test the model's ability to predict independent data that was not used in creating it. It is also useful to cope with problems like

K-fold is a common method for cross-validation. Firstly, all the data are divided into folds. Then, the learning model is created from the training set (k-1 folds), and the testing set (the fold left) is used for validation.

Normally, the folds obtained from the K-fold cross-validation are divided as equally as possible. Next, we are going to see the process of the K-fold cross-validation.