Xgb

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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

Options

options = a structure array with the following fields:

  • display: [ 'off' | {'on'} ] governs level of display to command window.
  • plots [ 'none' | {'final'} ] governs level of plotting.
  • waitbar: [ off | {'on'} ] governs display of waitbar during optimization and predictions.
  • preprocessing: {[] []}, two element cell array containing preprocessing structures (see PREPROCESS) defining preprocessing to use on the x- and y-blocks (first and second elements respectively)
  • algorithm: [ 'xgboost' ] algorithm to use. xgboost is default and currently only option.
  • classset : [ 1 ] indicates which class set in x to use when no y-block is provided.
  • xgbtype : [ {'xgbr'} | 'xgbc' ] Type of XGB to apply. Default is 'xgbc' for classification, and 'xgbr' for regression.
  • compression : [{'none'}| 'pca' | 'pls' ] type of data compression to perform on the x-block prior to calculaing or applying the XGB model. 'pca' uses a simple PCA model to compress the information. 'pls' uses either a pls or plsda model (depending on the xgbtype). Compression can make the XGB more stable and less prone to overfitting.
  • compressncomp : [ 1 ] Number of latent variables (or principal components to include in the compression model.
  • compressmd : [ 'no' |{'yes'}] Use Mahalnobis Distance corrected scores from compression model.
  • compressmd : [ 'no' |{'yes'}] Use Mahalnobis Distance correctedscores from compression model.
  • cvi : { { 'rnd' 5 } } Standard cross-validation cell (see crossval)defining a split method, number of splits, and number of iterations. This cross-validation is use both for parameter optimization and for error estimate on the final selected parameter values.Alternatively, can be a vector with the same number of elements as x has rows with integer values indicating CV subsets (see crossval).
  • eta : [{0.1}] Value(s) to use for XGBoost 'eta' parameter. Eta controls the learning rate of the gradient boosting.Values in range (0,1].
  • max_depth : [{6}] Value(s) to use for XGBoost 'max_depth' parameter. Specifies the maximum depth allowed for the decision trees.
  • num_round : [{500}] Value(s) to use for XGBoost 'num_round' parameter. Specifies how many rounds of tree creation to perform.

Algorithm

Xgb is implemented using the XGBoost XGBoost package. User-specified values are used for XGBoost parameters (see options above). See XGBoost Parameters for further details of these options.

The default XGB parameters eta, max_depth and num_round have value ranges rather than single values. This xgb function uses a search over the grid of appropriate parameters using cross-validation to select the optimal XGBoost parameter values and builds an XGB model using those values. This is the recommended usage. The user can avoid this grid-search by passing in single values for these parameters, however.

See Also

analysis, browse, knn, lwr, pls, plsda, xgbda, xgbengine