Builds an AdaBoost model on an H2OFrame.
h2o.adaBoost( x, y, training_frame, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), weights_column = NULL, nlearners = 50, weak_learner = c("AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING"), learn_rate = 0.5, weak_learner_params = NULL, seed = 1 )
x  (Optional) 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 or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. 
training_frame  Id of the training data frame. 
model_id  Destination id for this model; autogenerated if not specified. 
ignore_const_cols 

categorical_encoding  Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. 
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. Note: Weights are perrow observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but noninteger values are supported as well. During training, rows with higher weights matter more, due to the larger loss function prefactor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. 
nlearners  Number of AdaBoost weak learners. Defaults to 50. 
weak_learner  Choose a weak learner type. Defaults to AUTO, which means DRF. Must be one of: "AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING". Defaults to AUTO. 
learn_rate  Learning rate (from 0.0 to 1.0) Defaults to 0.5. 
weak_learner_params  Customized parameters for the weak_learner algorithm. E.g list(ntrees=3, max_depth=2, histogram_type='UniformAdaptive')) 
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 (timebased random number). 
Creates a H2OModel object of the right type.
predict.H2OModel
for prediction
if (FALSE) { library(h2o) h2o.init() # Import the airlines dataset f < "https://s3.amazonaws.com/h2opublictestdata/smalldata/prostate/prostate.csv" data < h2o.importFile(f) # Set predictors and response; set response as a factor data["CAPSULE"] < as.factor(data["CAPSULE"]) predictors < c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") response < "CAPSULE" # Train the AdaBoost model h2o_adaboost < h2o.adaBoost(x = predictors, y = response, training_frame = data, seed = 1234) }