XGB_CLASSIFIER

Trains an XGBoost model for classification on an input relation.

This is a meta-function. You must call meta-functions in a top-level SELECT statement.

Behavior type

Syntax

XGB_CLASSIFIER ('model-name', 'input-relation', 'response-column', 'predictor-columns'
        [ USING PARAMETERS
              [ exclude_columns = 'excluded-columns' ]
              [, max_ntree = max-trees ]
              [, max_depth = max-depth ]
              [, objective = 'optimization-strategy' ]
              [, learning_rate = learning-rate ]
              [, min_split_loss = minimum ]
              [, weight_reg = regularization ]
              [, nbins = num-bins ]
              [, sampling_size = fraction-of-rows ]
              [, col_sample_by_tree = sample-ratio-per-tree ]
              [, col_sample_by_node = sample-ratio-per-node ]
        ] )

Arguments

model-name

Name of the model (case-insensitive).

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 CHAR or VARCHAR that represents the dependent variable or outcome.

predictor-columns

Comma-separated list of columns to use from the input relation, or asterisk (*) to select all columns. Columns must be of data types CHAR, VARCHAR, BOOL, INT, or FLOAT.

Columns of type CHAR, VARCHAR, and BOOL are treated as categorical features; all others are treated as numeric features.

Vertica XGBoost and Random Forest algorithms offer native support for categorical columns (BOOL/VARCHAR). Simply pass the categorical columns as predictors to the models and the algorithm will automatically treat the columns as categorical and will not attempt to split them into bins in the same manner as numerical columns; Vertica treats these columns as true categorical values and does not simply cast them to continuous values under-the-hood.

Parameters

exclude_columns

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

max_ntree

Integer in the range [1,1000] that sets the maximum number of trees to create.

Default: 10

max_depth

Integer in the range [1,20] that specifies the maximum depth of each tree.

Default: 6

objective

The objective/loss function used to iteratively improve the model. Currently, ' crossentropy' is the only option.

Default: 'crossentropy'

learning_rate

Float in the range (0,1] that specifies the weight for each tree's prediction. Setting this parameter can reduce each tree's impact and thereby prevent earlier trees from monopolizing improvements at the expense of contributions from later trees.

Default: 0.3

min_split_loss

Float in the range [0,1000] that specifies the minimum amount of improvement each split must achieve on the model's objective function value to avoid being pruned.

If set to 0 or omitted, no minimum is set. In this case, trees are pruned according to positive or negative objective function values.

Default: 0.0 (disable)

weight_reg

Float in the range [0,1000] that specifies the regularization term applied to the weights of classification tree leaves. The higher the setting, the sparser or smoother the weights are, which can help prevent over-fitting.

Default: 1.0

nbins

Integer in the range (1,1000] that specifies the number of bins to use for finding splits in each column. More bins leads to longer runtime but more fine-grained and possibly better splits.

Default: 32

sampling_size

Float in the range (0,1] that specifies the fraction of rows to use in each training iteration.

A value of 1 indicates that all rows are used.

Default: 1.0

col_sample_by_tree

Float in the range (0,1] that specifies the fraction of columns (features), chosen at random, to use when building each tree.

A value of 1 indicates that all columns are used.

col_sample_by parameters "stack" on top of each other if several are specified. That is, given a set of 24 columns, for col_sample_by_tree=0.5 andcol_sample_by_node=0.5,col_sample_by_tree samples 12 columns, reducing the available, unsampled column pool to 12. col_sample_by_node then samples half of the remaining pool, so each node samples 6 columns.

This algorithm will always sample at least one column.

Default: 1

col_sample_by_node

Float in the range (0,1] that specifies the fraction of columns (features), chosen at random, to use when evaluating each split.

A value of 1 indicates that all columns are used.

This algorithm will always sample at least one column.

Default: 1

Examples

See XGBoost for classification.