Sometimes you want to compare two different databases that share a similar target variable, say, related to likability, but in a different scaling, say the one asked on a scale from 1 to 10 and the other used likert-scale from 1-7.

With nkululeko you can re-name labels, normalize the target values, and even inverse the polarity, for each databases.

In the following example there are two databases

[DATA]
databases = ['db1', 'db2']
db1.split_strategy = test
db1.scale = True
db2.colnames = {'non-attractive':'likability'}
db2.split_strategy = train
db2.scale = True
db2.reverse = True
db2.reverse.max = 10
target = likability
bins = [-1000, .2, 1000]
labels = ['less likable', 'more likable']

The one database db1 already has a likability label and just needs to be standard-normalized, the second one db2 has a related label non-attractive which needs to be renamed, inverted (based on a hypothetical maximum value of 10) and normalized.
Then, db1 can be used as test data and db2 as training.