SAP HANA Predictive Analysis Library (PAL)

3.2.13 Random Forest
The ransom forests algorithm is an ensemble learning method for classification or regression. It grows many
CART decision trees and outputs the class (classification) that is voted by majority of individual trees or mean
prediction (regression) of the individual trees.
The algorithm uses both bagging and random feature selection techniques. Each new training set is drawn
with replacement from the original training set, and then a tree is grown on the new training set using random
feature selection.
The random forest algorithm generates an internal unbiased estimate (out-of-bag error) of the generalization
error as the forest building processes. It gives estimates of what variables are important from nodes’ splitting
process. It also has an effective method for estimating missing data. If the m th variable is numerical, the
method computes the median of all values of this variable in class j or computes the most frequent nonmissing
value in class j, and then it uses this value to replace all missing values of the m th variable in class j.
Prerequisite
The target column of the training data must not have null values, and other columns should have at least one
valid value (not null).
RANDOMFORESTTRAIN
This function is used for classification or regression.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘RANDOMFORESTTRAIN’,
‘’, '', );
The signature table should contain the following records:
Table 159:
Position Schema Name Table Type Name Parameter Type
1
IN
2 IN
3
OUT
4
OUT
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 219