Train / test set for final evaluation
Untick this box to sort the data in descending order
By enabling time-based ordering, DSS checks that the train and the test sets are consistent with the time variable.
Moreover, DSS guarantees that:

Sampling & Splitting

If your dataset does not fit in your RAM, you may want to subsample the set on which splitting will be performed.

Partitions can be selected from the "Target" tab.

Test data is sub-sampled if necessary at max. 1M records.
Using a fixed random seed allows for reproducible result
{{uiState.splitMethodDesc}}
For more advanced splitting, use a split recipe, and then use "Explicit extracts from two datasets" policy
Gives error margins on metrics, but greatly increases training time
Approximate proportion of the sample that goes to the train set. The rest goes to the test set
Number of folds to divide the dataset into
Using a fixed random seed allows for reproducible splitting
Preserve {{ isCausalPrediction() | targetRoleName}} variable distribution within every split. See .
Rows with same group column value are assigned to same fold. See .
Column containing the k-fold groups

Train set

Test set

Train set

Test set

Metadata

Optional. Informative labels for the model. The model:algorithm, model:meta-learner, model:date, model:name, trainDataset:dataset-name, testDataset:dataset-name labels, evaluation:date and evaluationDataset:dataset-name are automatically added.