h2o.decision_tree function

Build a Decision Tree model

Build a Decision Tree model

Builds a Decision Tree model on an H2OFrame.

h2o.decision_tree( x, y, training_frame, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), seed = -1, max_depth = 20, min_rows = 10 )

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.
  • 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).
  • max_depth: Max depth of tree. Defaults to 20.
  • min_rows: Fewest allowed (weighted) observations in a leaf. Defaults to 10.

Returns

Creates a H2OModel object of the right type.

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 DT model h2o_dt <- h2o.decision_tree(x = predictors, y = response, training_frame = data, seed = 1234) ## End(Not run)

See Also

predict.H2OModel for prediction

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