Usage
h2o.gbm(x, y, training_frame, model_id = NULL, validation_frame = NULL,
nfolds = 0, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
score_each_iteration = FALSE, score_tree_interval = 0,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL,
weights_column = NULL, balance_classes = FALSE,
class_sampling_factors = NULL, max_after_balance_size = 5,
max_hit_ratio_k = 0, ntrees = 50, max_depth = 5, min_rows = 10,
nbins = 20, nbins_top_level = 1024, nbins_cats = 1024,
r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO",
"deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group",
"misclassification", "mean_per_class_error"), stopping_tolerance = 0.001,
max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE,
learn_rate = 0.1, learn_rate_annealing = 1, distribution = c("AUTO",
"bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie",
"laplace", "quantile", "huber"), quantile_alpha = 0.5,
tweedie_power = 1.5, huber_alpha = 0.9, checkpoint = NULL,
sample_rate = 1, sample_rate_per_class = NULL, col_sample_rate = 1,
col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1,
min_split_improvement = 1e-05, histogram_type = c("AUTO",
"UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin"),
max_abs_leafnode_pred = Inf, pred_noise_bandwidth = 0,
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen"))Arguments
x
A vector containing the names or indices of the predictor variables to use in building the model.
If x is missing,then all columns except y are used.
y
The name of the response variable in the model.If the data does not contain a header, this is the column index
number starting at 0, and increasing from left to right. (The response must be either an integer or a
categorical variable).
training_frame
Id of the training data frame (Not required, to allow initial validation of model parameters).
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
nfolds
Number of folds for N-fold cross-validation (0 to disable or >= 2). Defaults to 0.
keep_cross_validation_predictions
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical. Whether to keep the cross-validation fold assignment. Defaults to FALSE.
score_each_iteration
Logical. Whether to score during each iteration of model training. Defaults to FALSE.
score_tree_interval
Score the model after every so many trees. Disabled if set to 0. Defaults to 0.
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will
stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO",
"Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column
Column with cross-validation fold index assignment per observation.
ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.
offset_column
Offset column. This will be added to the combination of columns before applying the link function.
weights_column
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from
the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative
weights are not allowed.
balance_classes
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
FALSE.
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will
be automatically computed to obtain class balance during training. Requires balance_classes.
max_after_balance_size
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
balance_classes. Defaults to 5.0.
max_hit_ratio_k
Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
Defaults to 0.
ntrees
Number of trees. Defaults to 50.
max_depth
Maximum tree depth. Defaults to 5.
min_rows
Fewest allowed (weighted) observations in a leaf. Defaults to 10.
nbins
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Defaults to 20.
nbins_top_level
For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then
decrease by factor of two per level Defaults to 1024.
nbins_cats
For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher
values can lead to more overfitting. Defaults to 1024.
r2_stopping
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric
and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or
exceeds this Defaults to 1.797693135e+308.
stopping_rounds
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0.
stopping_metric
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) Must be one of:
"AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification",
"mean_per_class_error". Defaults to AUTO.
stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this
much) Defaults to 0.001.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
seed
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default)
Defaults to -1 (time-based random number).
build_tree_one_node
Logical. Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
Defaults to FALSE.
learn_rate
Learning rate (from 0.0 to 1.0) Defaults to 0.1.
learn_rate_annealing
Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) Defaults to 1.
distribution
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma",
"tweedie", "laplace", "quantile", "huber". Defaults to AUTO.
quantile_alpha
Desired quantile for Quantile regression, must be between 0 and 1. Defaults to 0.5.
tweedie_power
Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5.
huber_alpha
Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and
1). Defaults to 0.9.
checkpoint
Model checkpoint to resume training with.
sample_rate
Row sample rate per tree (from 0.0 to 1.0) Defaults to 1.
sample_rate_per_class
Row sample rate per tree per class (from 0.0 to 1.0)
col_sample_rate
Column sample rate (from 0.0 to 1.0) Defaults to 1.
col_sample_rate_change_per_level
Relative change of the column sampling rate for every level (from 0.0 to 2.0) Defaults to 1.
col_sample_rate_per_tree
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.
min_split_improvement
Minimum relative improvement in squared error reduction for a split to happen Defaults to 1e-05.
histogram_type
What type of histogram to use for finding optimal split points Must be one of: "AUTO", "UniformAdaptive",
"Random", "QuantilesGlobal", "RoundRobin". Defaults to AUTO.
max_abs_leafnode_pred
Maximum absolute value of a leaf node prediction Defaults to 1.797693135e+308.
pred_noise_bandwidth
Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions Defaults to 0.
categorical_encoding
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
"Binary", "Eigen". Defaults to AUTO.