Developer Guide

Contents

Details

Given
n
feature vectors
X
= {
x
1
= (
x
11
,...,
x
1
p
),...,
x
n
= (
x
n
1
,...,
x
np
) } of
n
p
-dimensional feature vectors and a vector of dependent variables
y
= (
y
1
,...,
y
n
), the problem is to build a gradient boosted trees regression model that minimizes the loss function based on the predicted and true value.

Training Stage

Gradient boosted trees regression follows the algorithmic framework of gradient boosted trees training with following loss functions: squared loss
.

Prediction Stage

Given the gradient boosted trees regression model and vectors
x
1
,...,
x
r
, the problem is to calculate responses for those vectors. To solve the problem for each given feature vector
x
i
, the algorithm finds the leaf node in a tree in the ensemble, and the leaf node gives the tree response. The algorithm result is a sum of responses of all the trees.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804