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. 

Dataset Info

  • 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.