Xgb: Difference between revisions
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imported>Scott (Created page with "===Purpose=== Gradient Boosted Tree (XGBoost) for regression or classification. ===Synopsis=== :model = xgb(x,y,options); %identifies model (calibration step) :pre...") |
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:model = xgb(x,y,options); %identifies model (calibration step) | :model = xgb(x,y,options); %identifies model (calibration step) | ||
:pred = xgb(x,model,options); %makes predictions with a new X-block | :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=== | ===Description=== | ||
Revision as of 17:33, 17 December 2018
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