h2o.isotonicregression {h2o}

Build an Isotonic Regression model

Description

Builds an Isotonic Regression model on an H2OFrame with a single feature (univariate regression).

Usage

h2o.isotonicregression(
  x,
  y,
  training_frame,
  model_id = NULL,
  validation_frame = NULL,
  weights_column = NULL,
  out_of_bounds = c("NA", "clip"),
  custom_metric_func = NULL,
  nfolds = 0,
  keep_cross_validation_models = TRUE,
  keep_cross_validation_predictions = FALSE,
  keep_cross_validation_fold_assignment = FALSE,
  fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
  fold_column = NULL
)

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.

validation_frame

Id of the validation data frame.

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.

out_of_bounds

Method of handling values of X predictor that are outside of the bounds seen in training. Must be one of: "NA", "clip". Defaults to NA.

custom_metric_func

Reference to custom evaluation function, format: 'language:keyName=funcName'

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.

keep_cross_validation_models

Logical. Whether to keep the cross-validation models. Defaults to TRUE.

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.

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.

Value

Creates a H2OModel object of the right type.

See Also

predict.H2OModel for prediction

Examples

## Not run: 
library(h2o)
h2o.init()

N <- 100
x <- seq(N)
y <- sample(-50:50, N, replace=TRUE) + 50 * log1p(x)

train <- as.h2o(data.frame(x = x, y = y))
isotonic <- h2o.isotonicregression(x = "x", y = "y", training_frame = train)

## End(Not run)