The Case for Learned Index proposes to replace data structures with machine learning models. The reasoning behind
this is that most data structures are general purpose, whereas a machine learning model can be specialized to a specefic dataset. We
propose to further specialize this idea by utilizing meta-learning. By looking at data characteristics called meta-features, we determined
the complexity of datasets. Several machine learning models were tested and ranked based on their performance using Multi-Criteria
Decision Analysis. A meta-learner was constructed which, based on the meta-features and the ranking of the machine learning models,
can predict which model to use for a given dataset. Furthermore, we introduced the notion that different applications require machine
learning models that excels at different aspects. Therefore, the user is able to specify which aspects their machine learning model
should excel at. Our results showed superior performance compared to the base learned index model presented by Kraska et al.