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 )
training_frame | Id of the training data frame. |
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x | A vector containing the |
model_id | Destination id for this model; auto-generated if not specified. |
score_each_iteration |
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score_tree_interval | Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
ignore_const_cols |
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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 |
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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 anonomaly_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. |
# NOT RUN { 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) # }