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Deprecated**KNIME H2O Machine Learning Integration** version **4.0.1.v201908131226** by **KNIME AG, Zurich, Switzerland**

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) .
- 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 along with sample_rate, then only the first option that DRF encounters will be used (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. 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) .
- Column sample rate (global)
- Specify the column sampling rate (y-axis). This acceptable value range is 0.0 to 1.0. Higher values may improve training accuracy (col_sample_rate) .
- 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) .
- Min number histogram bins (numerical)
- Specify the number of bins for the histogram to build, then split at the best point (nbins) .
- Max number root histogram bins (numerical)
- Specify the minimum 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 (nbins_top_level) .
- Learn 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 now try learn_rate=0.05 and learn_rate_annealing=0.99. This method would 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). The options are AUTO, bernoulli, multinomial, gaussian, poisson, gamma, laplace, quantile, huber, or tweedie (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. This option defaults to 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 for distribution) Specify the quantile to be used for Quantile Regression (quantile_alpha)
- Tweedie power
- (Only applicable if Tweedie is specified for distribution) Specify the Tweedie power. The range is from 1 to 2. For a normal distribution, enter 0. For Poisson distribution, enter 1. For a gamma distribution, enter 2. For a compound Poisson-gamma distribution, enter a value greater than 1 but less than 2. For more information, refer to Tweedie distribution (tweedie_power) .
- Huber alpha
- 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) .
- 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)

- Select categorical encoding
- Specify one of the following encoding schemes for handling categorical features (categorical_encoding) .
- Weight column selection
- 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) .
- Max Runtime?
- Maximum allowed runtime in seconds for model training (max_runtime_secs) .
- Use static random seed
- Select to use static seed for randomization.
- 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. 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 data-set used to evaluate early stopping criteria.

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To use this node in KNIME, install KNIME H2O Machine Learning Integration from the following update site:

KNIME 4.0

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