SAP HANA Predictive Analysis Library (PAL)

Newton method, Gradient Descend, BFGS and Cyclical Coordinate Descend (primarily for elastic net penalized
object function) are provided to minimize objective function which is opposite in sign with log likelihood
function. For fast convergence, Newton method and BFGS are preferred.
Elastic net regularization seeks to find coefficients which can minimize:
Where .
Here
and λ≥0. If α=0, we have the ridge regularization; if α=1, we have the LASSO regularization.
Function FORECASTWITHLOGISTICR is used to predict the labels for the testing data.
Prerequisites
●
●
●
No missing or null data in inputs.
Data is numeric, or categorical.
Given m independent variables, there must be at least m+1 records available for analysis.
LOGISTICREGRESSION
This is a logistic regression function.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘LOGISTICREGRESSION’,
‘’, '', );
The signature table should contain the following records:
Table 119:
Position Schema Name Table Type Name Parameter Type
1 IN
2 IN
3
OUT
OUT (optional)
5
OUT
166 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions