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) .

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