Project Details
Description
We represent the categorical predictor by a graph where the nodes are the categories and we establish a probability distribution over significant partitions of this graph.
Conditionally on the observed data, we obtain a posterior distribution for the aggregation of levels, which allows inferring about the most probable grouping for the categories. We draw inferences about all the other parameters of the regression model.
We compare our methods with the state-of-the-art and show that it has equally good predictive performance and more interpretable results.
Our approach balances accuracy against interpretability, a current major concern in statistics and machine learning.
Conditionally on the observed data, we obtain a posterior distribution for the aggregation of levels, which allows inferring about the most probable grouping for the categories. We draw inferences about all the other parameters of the regression model.
We compare our methods with the state-of-the-art and show that it has equally good predictive performance and more interpretable results.
Our approach balances accuracy against interpretability, a current major concern in statistics and machine learning.
| Status | Finished |
|---|---|
| Effective start/end date | 3/1/22 → 7/1/22 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Main Funding Source
- Installed Capacity (Academic Unit)
Location
- Bogotá D.C.
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