Xgb

Purpose

Gradient Boosted Tree (XGBoost) for regression or classification.

Synopsis

model = xgb(x,y,options); %identifies model (calibration step)

pred = xgb(x,model,options); %makes predictions with a new X-block

valid = xgb(x,y,model,options); %performs a "test" call with a new X-block and known y-values

Description

To choose between regression and classification, use the xgbtype option:

regression  : xgbtype = 'xgbr'

classification : xgbtype = 'xgbc'

It is recommended that classification be done through the xgbda function.

Inputs

• x = X-block (predictor block) class "double" or "dataset",
• y = Y-block (predicted block) class "double" or "dataset",
• model = previously generated model (when applying model to new data)

Outputs

• model = standard model structure containing the xgboost model (see Standard Model Structure). Feature scores are contained in model.detail.xgb.featurescores.

• pred = structure array with predictions
• valid = structure array with predictions