Batch Processing

Gradient boosted trees classification and regression follows the general workflow described in Usage Model: Training and Prediction .

Training

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:

  • inexact - continuous features are bucketed into discrete bins and the buckets borders are examined only
  • exact - all possible splits for a given feature are examined

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.

For more complete information about compiler optimizations, see our Optimization Notice.
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