Learns a Gradient Boosting Machine (GBM) regression model using H2O .

- Target Column
- Select target column. Must be numeric for regression 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) .
- Learning rate
- Specify the learning rate. The range is 0.0 to 1.0 (learn_rate) .

- Min (weighted) observations
- Specify the minimum number of observations for a leaf (min_rows) .
- Min relative 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) .
- Column sample rate (per tree)
- Specify the column sample rate per tree. This can be a value from 0.0 to 1.0. Note that it is multiplicative with col_sample_rate, so setting both parameters to 0.8, for example, results in 64% of columns being considered at any given node to split (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) GBM 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) .
- Column sample rate (global)
- Specify the column sampling rate (y-axis). The acceptable value range is 0.0 to 1.0. Higher values may improve training accuracy (col_sample_rate) .
- Learning rate annealing
- Specifies to reduce the learn_rate by this factor after every tree. So for N trees, GBM starts with learn_rate and ends with learn_rate * learn_rate_annealing^N. For example, instead of using learn_rate=0.01, you can learn_rate=0.05 and learn_rate_annealing=0.99. This method should converge much faster with almost the same accuracy. Use caution not to overfit (learn_rate_annlealing) .
- Distribution
- Specify the distribution (i.e., the loss function) (distribution) .
- Max absolute value of leaf node prediction
- When building a GBM classification model, this option reduces overfitting by limiting the maximum absolute value of a leaf node prediction. If set to 0, it is treated as Double.MAX_VALUE (max_abs_leafnode_pred) .
- Bandwidth of Gaussian multiplicative noise
- The bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions. If this parameter is specified with a value greater than 0, then every leaf node prediction is randomly scaled by a number drawn from a normal distribution centered around 1 with a bandwidth given by this parameter (pred_noise_bandwidth) .
- Quantile Alpha
- (Only applicable if Quantile is specified as distribution) Specify the quantile to be used for Quantile Regression (quantile_alpha)
- Tweedie Power
- (Only applicable if Tweedie is specified as distribution) Specify the Tweedie power. The range is from 1 to 2 (exclusive) (tweedie_power) .
- Huber Alpha
- (Only applicable if Huber is specified as distribution) Specify the desired quantile for Huber/M-regression (the threshold between quadratic and linear loss). This value must be between 0 and 1 (huber_alpha) .

- 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) .
- Use static random seed
- Select to use a static seed for randomization.
- Weights column (optional)
- Select a column to use for the observation weights which are used for bias correction (weights_column) .
- Offset column selection
- Specify a column to use as the offset. Note: Offsets are per-row “bias values” that are used during model training. (offset_column) .

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