The GBDT Binary Classification Prediction V2 component of Platform for AI (PAI) provides the prediction feature based on the GBDT Binary Classification V2 component. Gradient boosting decision trees are used to predict the binary classification results. This topic describes how to configure the GBDT Binary Classification Prediction V2 component.
Supported computing resources
You can use the GBDT Binary Classification Prediction V2 component based on the computing resources of MaxCompute and Flink.
Principle
The gradient boosting decision tree model consists of multiple decision trees. Each decision tree corresponds to a weak learner. Combining these weak learners together can achieve better classification and regression results.
The following figure shows the basic recursive structure of gradient boosting.
In most cases, 
is a CART decision tree, 
are the parameters of the decision tree, and 
is the step size. Each decision tree optimizes the objective function on the basis of the previous decision tree. After the preceding process, a model that contains multiple decision trees is obtained.
Configure the component in the PAI console
Input ports
Input port (from left to right)
Data type
Recommended upstream component
Required
Input
N/A
Yes
Predicted Data Table
N/A
Yes
Parameters
Tab
Parameter
Required
Description
Default value
Fields Information
Prediction result column name
Yes
The name of the prediction result column.
prediction_result
predictionDetailCol
No
The name of the prediction details column.
prediction_detail
Reserved Columns
No
The names of reserved columns. By default, all columns are reserved.
N/A
Tuning
Number of Instances
No
The number of instances that are used to run the job.
The value is automatically calculated based on the input data.
Memory Per Instance
No
The memory size of each instance. Unit: MB. Valid values: [100,65536].
The value is automatically calculated based on the input data.
Output ports
Port
Storage location
Recommended downstream component
Model type
Output
N/A
N/A
References
This component performs prediction based on the model trained by the GBDT Binary Classification V2 component.
For information about Machine Learning Designer components, see Overview of Machine Learning Designer.
Machine Learning Designer provides various preset algorithm components. You can select a component for data processing based on your business requirements. For more information, see Component reference: Overview of all components.