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
## Not run:library(h2o)h2o.init()# Import the prostate datasetp <- h2o.importFile(path="https://raw.github.com/h2oai/h2o/master/smalldata/logreg/prostate.csv")# Set the predictorspredictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON")# Build an Extended Isolation forest modelmodel <- h2o.extendedIsolationForest(x = predictors, training_frame = p, model_id ="eif.hex", ntrees =100, sample_size =256, extension_level = length(predictors)-1)# Calculate scorescore <- h2o.predict(model, p)anomaly_score <- score$anomaly_score
# Number in [0, 1] explicitly defined in Equation (1) from Extended Isolation Forest paper# or in paragraph '2 Isolation and Isolation Trees' of Isolation Forest paperanomaly_score <- score$anomaly_score
# Average path length of the point in Isolation Trees from root to the leafmean_length <- score$mean_length
## End(Not run)