Tree Methods
For training boosted tree models, there are 2 parameters used for choosing algorithms,
namely updater
and tree_method
. XGBoost has 3 builtin tree methods, namely
exact
, approx
and hist
. Along with these tree methods, there are also some
free standing updaters including refresh
, prune
and sync
. The parameter
updater
is more primitive than tree_method
as the latter is just a
preconfiguration of the former. The difference is mostly due to historical reasons that
each updater requires some specific configurations and might has missing features. As we
are moving forward, the gap between them is becoming more and more irrelevant. We will
collectively document them under tree methods.
Exact Solution
Exact means XGBoost considers all candidates from data for tree splitting, but underlying the objective is still interpreted as a Taylor expansion.
exact
: The vanilla gradient boosting tree algorithm described in reference paper. During splitfinding, it iterates over all entries of input data. It’s more accurate (among other greedy methods) but computationally slower in compared to other tree methods. Further more, its feature set is limited. Features like distributed training and external memory that require approximated quantiles are not supported. This tree method can be used with the parametertree_method
set toexact
.
Approximated Solutions
As exact
tree method is slow in computation performance and difficult to scale, we
often employ approximated training algorithms. These algorithms build a gradient
histogram for each node and iterate through the histogram instead of real dataset. Here
we introduce the implementations in XGBoost.
approx
tree method: An approximation tree method described in reference paper. It runs sketching before building each tree using all the rows (rows belonging to the root). Hessian is used as weights during sketch. The algorithm can be accessed by settingtree_method
toapprox
.hist
tree method: An approximation tree method used in LightGBM with slight differences in implementation. It runs sketching before training using only user provided weights instead of hessian. The subsequent pernode histogram is built upon this global sketch. This is the fastest algorithm as it runs sketching only once. The algorithm can be accessed by settingtree_method
tohist
.
Implications
Some objectives like reg:squarederror
have constant hessian. In this case, the
hist
should be preferred as weighted sketching doesn’t make sense with constant
weights. When using nonconstant hessian objectives, sometimes approx
yields better
accuracy, but with slower computation performance. Most of the time using hist
with
higher max_bin
can achieve similar or even superior accuracy while maintaining good
performance. However, as xgboost is largely driven by community effort, the actual
implementations have some differences than pure math description. Result might be
slightly different than expectation, which we are currently trying to overcome.
Other Updaters
Prune
: It prunes the existing trees.prune
is usually used as part of other tree methods. To use pruner independently, one needs to set the process type to update by:{"process_type": "update", "updater": "prune"}
. With this set of parameters, during training, XGBoost will prune the existing trees according to 2 parametersmin_split_loss (gamma)
andmax_depth
.Refresh
: Refresh the statistic of built trees on a new training dataset. Like the pruner, To use refresh independently, one needs to set the process type to update:{"process_type": "update", "updater": "refresh"}
. During training, the updater will change statistics likecover
andweight
according to the new training dataset. Whenrefresh_leaf
is also set to true (default), XGBoost will update the leaf value according to the new leaf weight, but the tree structure (split condition) itself doesn’t change.There are examples on both training continuation (adding new trees) and using update process on
demo/guidepython
. Also checkout theprocess_type
parameter in XGBoost Parameters.Sync
: Synchronize the tree among workers when running distributed training.
Removed Updaters
3 Updaters were removed during development due to maintainability. We describe them here solely for the interest of documentation.
Distributed colmaker, which was a distributed version of exact tree method. It required specialization for column based splitting strategy and a different prediction procedure. As the exact tree method is slow by itself and scaling is even less efficient, we removed it entirely.
skmaker
. Pernode weighted sketching employed bygrow_local_histmaker
is slow, theskmaker
was unmaintained and seems to be a workaround trying to eliminate the histogram creation step and uses sketching values directly during split evaluation. It was never tested and contained some unknown bugs, we decided to remove it and focus our resources on more promising algorithms instead. For accuracy, most of the timeapprox
andhist
are enough with some parameters tuning, so removing them don’t have any real practical impact.grow_local_histmaker
updater: An approximation tree method described in reference paper. This updater was rarely used in practice so it was still an updater rather than tree method. During split finding, it first runs a weighted GK sketching for data points belong to current node to find split candidates, using hessian as weights. The histogram is built upon this pernode sketch. It was faster thanexact
in some applications, but still slow in computation. It was removed because it depended on Rabit’s customized reduction function that handles all the data structure that can be serialized/deserialized into fixed size buffer, which is not directly supported by NCCL or federated learning gRPC, making it hard to refactor into a common allreducer interface.
Feature Matrix
Following table summarizes some differences in supported features between 4 tree methods, T means supported while F means unsupported.
Exact 
Approx 
Approx (GPU) 
Hist 
Hist (GPU) 


grow_policy 
Depthwise 
depthwise/lossguide 
depthwise/lossguide 
depthwise/lossguide 
depthwise/lossguide 
max_leaves 
F 
T 
T 
T 
T 
sampling method 
uniform 
uniform 
gradient_based/uniform 
uniform 
gradient_based/uniform 
categorical data 
F 
T 
T 
T 
T 
External memory 
F 
T 
P 
T 
P 
Distributed 
F 
T 
T 
T 
T 
Features/parameters that are not mentioned here are universally supported for all 3 tree methods (for instance, column sampling and constraints). The P in external memory means special handling. Please note that both categorical data and external memory are experimental.