H2O Random Forest Learner

Learns a Distributed Random Forest (DRF) classification model, which is a special version of the random forest* algorithm provided by H2O .

(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.

Options

General Settings

Target Column: Select target column. Must be nominal for classification problems.
Column selection: Select columns used for model training.
Ignore constant columns: Select to ignore constant columns.
Number of levels (tree depth): Specify the maximum tree depth (max_depth) .
Number of models: Specify the number of trees (ntrees) .
Use static random seed: Select to use a static seed for randomization.

Algorithm Settings

Min (weighted) observations: Specify the minimum number of observations for a leaf (min_rows) .
Min relative split improvement rate: The value of this option specifies the minimum relative improvement in squared error reduction in order for a split to happen. When properly tuned, this option can help reduce overfitting. Optimal values would be in the 1e-10...1e-3 range (min_split_improvement) .
Row sample rate (per tree): Specify the row sampling rate (x-axis). The range is 0.0 to 1.0. Higher values may improve training accuracy. Test accuracy improves when either columns or rows are sampled. For details, refer to “Stochastic Gradient Boosting” (sample_rate) .
Class specific sample rate (per tree): When building models from imbalanced datasets, this option specifies that each tree in the ensemble should sample from the full training dataset using a per-class-specific sampling rate rather than a global sample factor (as with sample_rate). The range for this option is 0.0 to 1.0. If this option is specified, sample_rate is disabled (sample_rate_per_class) .
Column sample rate (per tree): Specify the column sample rate per tree. This can be a value from 0.0 to 1.0 (col_sample_rate_per_tree) .
Relative change of column sample rate per level: This option specifies to change the column sampling rate as a function of the depth in the tree (col_sample_rate_change_per_level) .
Histogram type: By default (AUTO) DRF bins from min...max in steps of (max-min)/N. Random split points or quantile-based split points can be selected as well (histogram_type) .
Number of histogram bins (numerical): Specify the number of bins for the histogram to build, then split at the best point (nbins) .
Number of histogram bins (categorical): Specify the number of bins for the histogram to build, then split at the best point. Higher values can lead to more overfitting. The levels are ordered alphabetically; if there are more levels than bins, adjacent levels share bins. This value has a more significant impact on model fitness than nbins. Larger values may increase runtime, especially for deep trees and large clusters, so tuning may be required to find the optimal value for your configuration (nbins_cats) .
Number of root histogram bins (numerical): Specify the number of bins at the root level to use to build the histogram. This number will then be decreased by a factor of two per level, whereby nbins controls when to stop dividing (nbins_top_level) .
Binominal double trees: (Binary classification only) Build twice as many trees (one per class). Enabling this option can lead to higher accuracy, while disabling can result in faster model building. This option is disabled by default (binominal_double_trees) .
M Tries: Specify the columns to randomly select at each level. If disabled, the number of variables is the square root of p for classification and p/3 for regression (where p is the number of columns). The range is 1 to p. (mtries) .

Advanced Settings

Select categorical encoding: Specify one of the following encoding schemes for handling categorical features (categorical_encoding) .
Early Stopping: Select to activate early stopping.
Stopping metric: Specify the metric to use for early stopping (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. To disable this feature, specify 0. If disabled, the metric is computed on the validation data (if provided); otherwise, training data is used (stopping_rounds) .
Size of validation set (in %): Specify the size of the validation dataset used to evaluate early stopping criteria.
Max runtime in seconds: Maximum allowed runtime in seconds for model training (max_runtime_secs) .
Weights column (optional): Select a column to use for the observation weights which are used for bias correction (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 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) .

Input Ports

: H2O Frame with training data.

Output Ports

: Variable importance in tabular format.
: H2O Distributed Random Forest classification model.

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Installation

To use this node in KNIME, install the extension KNIME H2O Machine Learning Integration from the below update site following our NodePit Product and Node Installation Guide:

v5.4

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.4.0.v202407231410

On NodePit since: 2024-12-06

Last update: 2024-12-17

KNIME versions: Since v4.0

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