H2O AutoML Learner

Learns the specified types of models using H2O AutoML and returns the leading model amongst these. As part of the learning process, hyperparameters are automatically optimized by H2O using a random grid search.

Options

General Settings

Target Column

Select target column. The column must contain nominal values.

Column selection

Select columns used for model training.

Max. runtime in seconds

Select to specify the maximum runtime for AutoML learning in seconds. Note that this setting can affect AutoML reproducibility (max_runtime_secs) .

Max number of models

Select to specify the maximum number of models that should be trained, excluding Stacked Ensemble models (max_models) .

Use static random seed

Select to use static seed for randomization.

Algorithm Settings

Scoring metric used to select best model

Select the metric used to sort the leaderboard at the end of an AutoML run. The leading model according to the metric will be returned (sort_metric) .

Include algorithms

Select the algorithms that should be included in the AutoML run. Note that Deep Learning means a multi-layer feedforward artificial neural network. If Stacked Ensemble is checked, a second-level model is learned that stacks/combines the learned and optimized models. Hence, Stacked Ensemble can only be included if at least one other model type is included (include_algos) .

Number of folds

The number of folds that should be used for k-fold cross-validation of the models in the AutoML run (nfolds) .

Use fold column

Select to specify a column with cross-validation fold index assignment per observation. The column must not be the same column as the target column and must either contain integer or nominal values (fold_column) .

Advanced Settings

Early Stopping

Select to activate early stopping.

Stopping metric

Specify the metric to use for early stopping. The metric is calculated on the cross-validation folds (stopping_metric) .

Stopping tolerance

Specify the relative tolerance for the metric-based stopping to stop training if the improvement is less than this value (stopping_tolerance) .

Number of last seen rows for moving average

Stops training when the option selected for stopping_metric doesn’t improve for the specified number of training rounds, based on a simple moving average. The metric is calculated on the cross-validation folds (stopping_rounds) .

Max. runtime in seconds per model

Specify the maximum amount of time dedicated to the training of each individual model in the AutoML run. This setting can affect AutoML reproducibility (max_runtime_secs_per_model) .

Weight column (optional)

Select a column to use for the observation weights which are used for bias correction. Note that this setting can affect AutoML reproducibility slightly (weights_column) .

Balance classes

Oversample the minority classes to balance the class distribution. This option is not enabled by default and can increase the data frame size (balance_classes) .

Define max relative number of rows after balancing

This specifies the maximum relative size of the training data after balancing class counts (max_after_balance_size) .

Class specific sampling factors

Specify the per-class (in lexicographical order) over/under-sampling ratios. By default, these ratios are automatically computed during training to obtain the class balance (class_sampling_factors) .

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Workflows

• !Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub
• 0_install_AI_extensionsKNIME Hub
• 01_AutoML_Component_via_Interactive_ViewsKNIME Hub
• 02_AutoML_Regression_and_Classification_ExamplesKNIME Hub
• 02_AutoML_Regression_and_Classification_ExamplesKNIME Hub
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