The Ridge Regression Prediction component supports sparse and dense data. You can use this component to estimate values of numeric variables, such as housing prices, sales volumes, and temperatures. This topic describes how to configure the Ridge Regression Prediction component.
Limits
The supported computing engines are MaxCompute, Apache Flink or DLC.
How Tikhonov regularization works
Tikhonov regularization is a biased estimation regression method dedicated to the analysis of collinearity data. It is essentially an improved least squares method. By giving up the unbiasedness of the least squares method, Tikhonov regularization is more realistic and reliable to obtain regression coefficients and fits better with ill-conditioned data than the least squares method. However, Tikhonov regularization also causes partial information loss and reduced accuracy.
Configure the component in the PAI console
Input ports
Input port (left-to-right)
Data type
Recommended upstream component
Required
Input model of the prediction
None
Yes
Input data of the prediction
None
Yes
Component parameters
Tab
Parameter
Description
Field Setting
reservedCols
The columns to be reserved by the algorithm.
vectorCol
The name of the vector column.
Parameter Setting
predictionCol
The name of the prediction column.
numThreads
The number of threads of the component. Default value: 1.
Execution Tuning
Number of Workers
The number of workers. This parameter must be used together with the Memory per worker, unit MB parameter. The value of this parameter must be a positive integer. Valid values: [1,9999].
Memory per worker, unit MB
The memory size of each worker. Valid values: 1024 to 64 × 1024. Unit: MB.
Configure the component by coding
You can copy the following code to the code editor of the PyAlink Script component. This allows the PyAlink Script component to function like the Ridge Regression Prediction component.
from pyalink.alink import *
def main(sources, sinks, parameter):
model = sources[0]
batchData = sources[1]
predictor = RidgeRegPredictBatchOp()\
.setPredictionCol("pred")
result = predictor.linkFrom(model, batchData)
result.link(sinks[0])
BatchOperator.execute()