To validate your model following techniques are available:

It is running k-fold cross-validation. It accepts parameters:

• k_folds - the number of folds in cross-validation. The default is set to 5.
• stratify - decides if samples will be distributed equally in classes between training and validation subsets. It is available only for classification tasks. The default is set to False.
• shuffle - decides if shuffle samples before training, The default set to True.
• random_seed - a seed

To use this validation please set validation_type="kfold". In this validation scenario, there will be created k_folds of machine learning models (if you set k_folds=5 then as a result 5 models will be trained on different portions of data).

{
  "validation_type": "kfold",
  "k_folds": 5,
  "shuffle": True,
  "stratify": False,
  "random_seed": 123
}

It accepts parameters:

• train_ratio - what ratio of samples should be used for training. Should be in 0 - 1 range.
• stratify - decides if samples will be distributed equally in classes between training and validation subsets. It is available only for classification tasks. The default is set to False.
• shuffle - decides if shuffle samples before training, The default set to True.
• random_seed - a seed

To use this validation please set validation_type="split". In this validation scenario, there will be created one machine learning model.

{
  "validation_type": "split",
  "train_ratio": 0.8,
  "shuffle": True,
  "stratify": False,
  "random_seed": 123
}

To use this validation please set validation_type="with_dataset". In this validation scenario, there will be created one machine learning model. Please make sure that you provide train and validation datasets. There should be different datasets, not the same!

{
  "validation_type": "with_dataset",
}