Trains an Isolation Forest model

h2o.isolationForest(
  training_frame,
  x,
  model_id = NULL,
  score_each_iteration = FALSE,
  score_tree_interval = 0,
  ignore_const_cols = TRUE,
  ntrees = 50,
  max_depth = 8,
  min_rows = 1,
  max_runtime_secs = 0,
  seed = -1,
  build_tree_one_node = FALSE,
  mtries = -1,
  sample_size = 256,
  sample_rate = -1,
  col_sample_rate_change_per_level = 1,
  col_sample_rate_per_tree = 1,
  categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
    "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
  stopping_rounds = 0,
  stopping_metric = c("AUTO", "anomaly_score"),
  stopping_tolerance = 0.01,
  export_checkpoints_dir = NULL,
  contamination = -1,
  validation_frame = NULL,
  validation_response_column = NULL
)

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.
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.
ignore_const_cols	`Logical`. Ignore constant columns. Defaults to TRUE.
ntrees	Number of trees. Defaults to 50.
max_depth	Maximum tree depth (0 for unlimited). Defaults to 8.
min_rows	Fewest allowed (weighted) observations in a leaf. Defaults to 1.
max_runtime_secs	Maximum allowed runtime in seconds for model training. Use 0 to disable. 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).
build_tree_one_node	`Logical`. Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. Defaults to FALSE.
mtries	Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3. Defaults to -1.
sample_size	Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored. Defaults to 256.
sample_rate	Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead. Defaults to -1.
col_sample_rate_change_per_level	Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to 1.
col_sample_rate_per_tree	Column sample rate per tree (from 0.0 to 1.0) Defaults to 1.
categorical_encoding	Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
stopping_rounds	Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0.
stopping_metric	Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "anomaly_score". Defaults to AUTO.
stopping_tolerance	Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.01.
export_checkpoints_dir	Automatically export generated models to this directory.
contamination	Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined). Defaults to -1.
validation_frame	Id of the validation data frame.
validation_response_column	(experimental) Name of the response column in the validation frame. Response column should be binary and indicate not anomaly/anomaly.

Examples

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

# Import the cars dataset
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv"
cars <- h2o.importFile(f)

# Set the predictors
predictors <- c("displacement", "power", "weight", "acceleration", "year")

# Train the IF model
cars_if <- h2o.isolationForest(x = predictors, training_frame = cars,
                               seed = 1234, stopping_metric = "anomaly_score",
                               stopping_rounds = 3, stopping_tolerance = 0.1)
}