This brick provides you with an easy interface for creating your own out-of-box and distributed gradient boosted decision tree for your multiclass classification tasks (you can use this brick for binary classification as well, but we would recommend using LBGM Binary instead). Due to its leaf-wise processing nature, the created model can be easily trained on large datasets, while giving formidable results.
The models are built on three important principles:
In this case, the weak learners are multiple sequential specialized decision trees, which do the following things:
All those trees are trained by propagating the gradients of errors throughout the system.
The main drawback of the LGBM Binary is that finding the best split points in each tree node is both a time-consuming and memory-consuming operation.
Bricks → Analytics → Data Mining / AI → Classification Models → LGBM Multiclass
Learning rate
Boosting learning rate. This parameter controls how quickly or slowly the algorithm will learn a problem. Generally, a bigger learning rate will allow a model to learn faster while a smaller learning rate will lead to a more optimal outcome.
Number of iterations
A number of boosting iterations. This parameter is recommended to be set inversely to the learning rate selected (decrease one while increasing second).
Number of leaves
The main parameter to control model complexity. Higher values should increase accuracy but might lead to overfitting.
Prediction mode
This parameter specifies the model's prediction format of the target variable: