Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
Gradient boosted trees classification and regression follows the general workflow described in Usage Model: Training and Prediction .
For description of the input and output, refer to Usage Model: Training and Prediction.
At the training stage, the gradient boosted trees batch algorithm has the following parameters:
Parameter |
Default Value |
Description |
|
---|---|---|---|
splitMethod |
inexact |
Split computation mode. Possible values:
|
|
maxIterations |
50 |
Maximal number of iterations when training the model, defines maximal number of trees in the model. |
|
maxTreeDepth |
6 |
Maximal tree depth. If the parameter is set to 0 then the depth is unlimited. |
|
shrinkage |
0.3 |
Learning rate of the boosting procedure. Scales the contribution of each tree by a factor (0, 1] |
|
minSplitLoss |
0 |
Loss regularization parameter. Minimal loss reduction required to make a further partition on a leaf node of the tree. Range: [0,∞) |
|
lambda |
1 |
L2 regularization parameter on weights. Range: [0, ∞) |
|
observationsPerTreeFraction |
1 |
Fraction of the training set S used for a single tree training, 0 < observationsPerTreeFraction ≤ 1. The observations are sampled randomly without replacement. |
|
featuresPerNode |
0 |
Number of features tried as the possible splits per node. If the parameter is set to 0, all features are used. |
|
minObservationsInLeafNode |
5 |
Minimal number of observations in the leaf node. |
|
memorySavingMode |
false |
If true then use memory saving (but slower) mode. |
|
engine |
SharePtr< engines:: mt19937:: Batch>() |
Pointer to the random number generator. |
|
maxBins |
256 |
Used with inexact split method only. Maximal number of discrete bins to bucket continuous features. Increasing the number results in higher computation costs |
|
minBinSize |
5 |
Used with inexact split method only. Minimal number of observations in a bin. |