predict.GBMCVFit

Calculate predictions from cross validated generalized boosting
model from gbm2.

Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund
and R. Schapire (1997) <doi:10.1006/jcss.1997.1504> and Friedman's gradient
boosting machine, J.H. Friedman (2001) <doi:10.1214/aos/1013203451>.
Includes regression methods for least squares, absolute loss,
t-distribution loss, quantile regression, logistic, Poisson,
Cox proportional hazards partial likelihood, AdaBoost
exponential loss, Huberized hinge loss,
and Learning to Rank measures (LambdaMART).

Ridgeway Greg

gbm3

Generalized Boosted Regression Models

James Hickey

Paul Metcalfe

Greg Ridgeway

Stefan Schroedl

Harry Southworth

Terry Therneau

predict.GBMCVFit function

<dl><dt>object</dt>
<dd>a GBMCVFit object containing CV gbm models</dd>
<dt>gbm_data_obj</dt>
<dd>a GBMData object containing all of the data
used to fit a gbm model.</dd>
<dt>cv_folds</dt>
<dd>a positive integer specifying the number of folds
to be used in cross-validation of the gbm fit.</dd>
<dt>cv_group</dt>
<dd>vector of integers specifying which row of data
belongs to which cv_fold.</dd>
<dt>best_iter_cv</dt>
<dd>number of trees with the smallest cv error</dd>
<dt>...</dt>
<dd>not currently used</dd></dl>

Arguments

Author

Predictions for CV fitted GBM models — predict.GBMCVFit

<dl>

<dt>object</dt>
<dd>a GBMCVFit object containing CV gbm models</dd>


<dt>gbm_data_obj</dt>
<dd>a GBMData object containing all of the data
used to fit a gbm model.</dd>


<dt>cv_folds</dt>
<dd>a positive integer specifying the number of folds
to be used in cross-validation of the gbm fit.</dd>


<dt>cv_group</dt>
<dd>vector of integers specifying which row of data
belongs to which cv_fold.</dd>


<dt>best_iter_cv</dt>
<dd>number of trees with the smallest cv error</dd>


<dt>...</dt>
<dd>not currently used</dd>

</dl>

predict.GBMCVFit: Predictions for CV fitted GBM models

Description

Usage

Value

Arguments

Author