h2o (version 3.44.0.3)

h2o.extendedIsolationForest: Trains an Extended Isolation Forest model

Description

Trains an Extended Isolation Forest model

Usage

h2o.extendedIsolationForest(
  training_frame,
  x,
  model_id = NULL,
  ignore_const_cols = TRUE,
  categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
    "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
  score_each_iteration = FALSE,
  score_tree_interval = 0,
  ntrees = 100,
  sample_size = 256,
  extension_level = 0,
  seed = -1,
  disable_training_metrics = TRUE
)

Arguments

training_frame

Id of the training data frame.

x

A vector containing the character names of the predictors in the model.

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.

score_each_iteration

Logical. Whether to score during each iteration of model training. Defaults to FALSE.

score_tree_interval

Score the model after every so many trees. Disabled if set to 0. Defaults to 0.

ntrees

Number of Extended Isolation Forest trees. Defaults to 100.

sample_size

Number of randomly sampled observations used to train each Extended Isolation Forest tree. Defaults to 256.

extension_level

Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with extension_Level = 0 behaves like Isolation Forest. Defaults to 0.

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).

disable_training_metrics

Logical. Disable calculating training metrics (expensive on large datasets) Defaults to TRUE.

Examples

if (FALSE) {
library(h2o)
h2o.init()

# Import the prostate dataset
p <- h2o.importFile(path="https://raw.github.com/h2oai/h2o/master/smalldata/logreg/prostate.csv")

# Set the predictors
predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON")

# Build an Extended Isolation forest model
model <- h2o.extendedIsolationForest(x = predictors,
                                     training_frame = p,
                                     model_id = "eif.hex",
                                     ntrees = 100,
                                     sample_size = 256,
                                     extension_level = length(predictors) - 1)

# Calculate score
score <- h2o.predict(model, p)
anomaly_score <- score$anomaly_score

# Number in [0, 1] explicitly defined in Equation (1) from Extended Isolation Forest paper
# or in paragraph '2 Isolation and Isolation Trees' of Isolation Forest paper
anomaly_score <- score$anomaly_score

# Average path length of the point in Isolation Trees from root to the leaf
mean_length <- score$mean_length
}