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; auto-generated if not specified.
ignore_const_cols: Logical. Ignore constant columns. Defaults to TRUE.
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 per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. 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 (time-based random number).
Returns
Creates a H2OModel object of the right type.
Examples
## Not run:library(h2o)h2o.init()# Import the airlines datasetf <-"https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv"data <- h2o.importFile(f)# Set predictors and response; set response as a factordata["CAPSULE"]<- as.factor(data["CAPSULE"])predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON")response <-"CAPSULE"# Train the AdaBoost modelh2o_adaboost <- h2o.adaBoost(x = predictors, y = response, training_frame = data, seed =1234)## End(Not run)