RF_CLASSIFIER

Trains a random forest model for classification on an input relation.

Syntax

RF_CLASSIFIER ( 'model‑name', input‑relation, 'response‑column', 'predictor‑columns'                                                          
             [ USING PARAMETERS [exclude_columns='excluded‑columns']    
                                [, ntree=num‑trees]
                                [, mtry=num‑features]
                                [, sampling_size=sampling‑size]
                                [, max_depth=depth]
                                [, max_breadth=breadth]
                                [, min_leaf_size=leaf_size]
                                [, min_info_gain=threshold]
                                [, nbins=num‑bins] ] )

Arguments

model‑name

Identifies the model stored as a result of the training, where model‑name conforms to conventions described in Identifiers. It must also be unique among all names of sequences, tables, projections, views, and models within the same schema.
input‑relation

The table or view that contains the training samples. If the input relation is defined in Hive, use SYNC_WITH_HCATALOG_SCHEMA to sync the hcatalog schema, and then run the machine learning function.
response‑column

An input column of type numeric, CHAR/VARCHAR, or BOOLEAN that represents the dependent variable.

Vertica automatically casts numeric response column values to VARCHAR.

predictor‑columns

Comma-separated list of columns in the input relation that represent independent variables for the model, or asterisk (*) to select all columns. If you select all columns, the argument list for parameter exclude_columns must include response‑column, and any columns that are invalid as predictor columns.

All predictor columns must be of type numeric, CHAR/VARCHAR, or BOOLEAN; otherwise the model is invalid. BOOLEAN column values are converted to FLOAT values before training: 0 for false, 1 for true.

Parameter Settings

Parameter name Set to…
exclude_columns

Comma-separated list of column names from input‑columns to exclude from processing.
ntree

The number of trees in the forest, an integer between 0 and 1000, inclusive.

Default: 20
mtry

The number of randomly chosen features from which to pick the best feature to split on a given tree node, an integer ≤ number‑predictors.

Default: Square root of the total number of predictors
sampling_size

The portion of the input data set that is randomly picked for training each tree, a FLOAT between 0.0 and 1.0, inclusive.

Default: 0.632
max_depth

The maximum depth for growing each tree, an integer between 1 and 100, inclusive.

Default: 5
max_breadth

The maximum number of leaf nodes a tree in the forest can have, an integer between 1 and 1e9, inclusive.

Default: 32
min_leaf_size

The minimum number of samples each branch must have after splitting a node, an integer between 1 and 1e6, inclusive. A split that causes fewer remaining samples is discarded.

Default: 1
min_info_gain

The minimum threshold for including a split, a FLOAT between 0.0 and 1.0, inclusive. A split with information gain less than this threshold is discarded.

Default: 0.0
nbins

The number of bins to use for continuous features, an integer between 2 and 1000, inclusive.

Default: 32

Model Attributes

Attribute Description
data

Data for the function, including:

  • predictorNames: The name of the predictors in the same order they were specified for training the model.
  • predictorTypes: The type of the predictors in the same order as their names in predictorNames.
ntree

Number of trees in the model.
skippedRows Number of rows in input_relation that were skipped because they contained an invalid value.
processedRows Total number of rows in input_relation minus skippedRows.
callStr Value of all input arguments that were specified at the time the function was called.

Examples

=> SELECT RF_CLASSIFIER ('myRFModel', 'iris', 'Species', 'Sepal_Length, Sepal_Width, 
Petal_Length, Petal_Width' USING PARAMETERS ntree=100, sampling_size=0.3);

RF_CLASSIFIER
--------------------------------------------------
Finished training
(1 row)

See Also