h2o.isotonicregression function

Build an Isotonic Regression model

Build an Isotonic Regression model

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

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.

Returns

Creates a H2OModel object of the right type.

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)

See Also

predict.H2OModel for prediction

  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11