Build an AdaBoost model

Source: R/adaboost.R

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"),
  learn_rate = 0.5,
  seed = -1
)

Arguments

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". Defaults to AUTO.
learn_rate

Learning rate (from 0.0 to 1.0) Defaults to 0.5.
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).

Value

Creates a H2OModel object of the right type.

See also

predict.H2OModel for prediction

Examples

# NOT RUN {
library(h2o)
h2o.init()

# Import the airlines dataset
f <- "https://s3.amazonaws.com/h2o-public-test-data/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)
# }