  Gradient boosted decision trees (GBDT) is the leading algorithm for many
commercial and academic data applications. We give a deep analysis of this
algorithm, especially the histogram technique, which is a basis for the
regulized distribution with compact support. We present three new
modifications. 1) Share memory technique to reduce memory usage. In many cases,
it only need the data source itself and no extra memory. 2) Implicit merging
for "merge overflow problem"."merge overflow" means that merge some small
datasets to huge datasets, which are too huge to be solved. By implicit
merging, we just need the original small datasets to train the GBDT model. 3)
Adaptive resize algorithm of histogram bins to improve accuracy. Experiments on
two large Kaggle competitions verified our methods. They use much less memory
than LightGBM and have higher accuracy. We have implemented these algorithms in
an open-source package LiteMORT. The source codes are available at
https://github.com/closest-git/LiteMORT
