As explained in the Cross-validation dataset in the Data Engine section, cross-validation is automatically applied when the dataset is split between training and testing. Cross-Validation Details in the ML Engine is where you can see the details of the cross-validation performed on the data.
Click on the Cross-Validation Details tab. The details shown are explained below.
Raw dataset: the raw data used for modelling
# of rows: number of rows in raw data
# of Features: number of columns or features in raw data
Target Feature: Target feature
Dataset Name: dataset created from raw data and used to for modelling
Training Set Details
Samples(#): number of rows in the training dataset
Used samples(%): percentage of data used for training
Test Set Details
Samples(#): number of rows in testing or validation dataset
Use samples(%): percentage of the dataset used for testing or validation.