Details

Given n feature vectors of -dimensional feature vectors and responses , the problem is to build a gradient boosted trees classification or regression model.

The tree ensemble model uses M additive functions to predict the output where is the space of regression trees, is the number of leaves in the tree, is a leaf weights vector, is a score on -th leaf. represents the structure of each tree that maps an observation to the corresponding leaf index.

where is twice differentiable convex loss function and is a regularization term that penalizes the complexity of the model defined by the number of leaves T and the L2 norm of the weights for each tree, and are regularization parameters.

where , and following algorithmic framework for the training stage.

Let be the set of observations. Given the training parameters, such as the number of iterations , loss function , regression tree training parameters, regularization parameters and , shrinkage (learning rate) parameter , the algorithm does the following:

Find an initial guess ,
For :
Update and ,
Grow a regression tree that minimizes the objective function , where , , , .
Assign an optimal weight to the leaf , .
Apply shrinkage parameter to the tree leafs and add the tree to the model
Update

Generate a bootstrap training set if required (stochastic gradient boosting) as follows: select randomly without replacement observations, where is a fraction of observations used for training of one tree.
Start from the tree with depth .
For each leaf node in the tree:
Choose a subset of feature for split finding if required (stochastic gradient boosting).
Find the best split that maximizes the gain:

Minimal number of observations in a leaf node.
Node t is not processed if the subset of observations is smaller than the predefined value. Splits that produce nodes with the number of observations smaller than that value are not allowed.
Maximal tree depth.
Node t is not processed, if its depth in the tree reached the predefined value.
Minimal split loss.
Node t is not processed, if the best possible split is smaller than parameter .

Given a gradient boosted trees model and vectors , the problem is to calculate the responses for those vectors. To solve the problem for each given query vector , the algorithm finds the leaf node in a tree in the ensemble which gives the response by that tree. Resulting response is based on an aggregation of responses from all trees in the ensemble. For detailed definition, see description of a specific algorithm.

exact - all possible split values are examined when searching for the best split for a feature.
inexact - continuous features are bucketed into discrete bins and the possible splits are restricted by the buckets borders only.

Batch Processing

Training Parameters for Gradient Boosted Trees (Batch Processing)

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		Maximal number of iterations when training the model, defines maximal number of trees in the model.
maxTreeDepth		Maximal tree depth. If the parameter is set to then the depth is unlimited.
shrinkage		Learning rate of the boosting procedure. Scales the contribution of each tree by a factor
minSplitLoss		Loss regularization parameter. Minimal loss reduction required to make a further partition on a leaf node of the tree. Range:
lambda		L2 regularization parameter on weights. Range:
observationsPerTreeFraction		Fraction of the training set S used for a single tree training, . The observations are sampled randomly without replacement.
featuresPerNode		The number of features tried as the possible splits per node. If the parameter is set to , all features are used.
minObservationsInLeafNode		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		Used with inexact split method only. Maximal number of discrete bins to bucket continuous features. Increasing the number results in higher computation costs
minBinSize		Used with inexact split method only. Minimal number of observations in a bin.