- 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).