Fitting Prediction Rule Ensembles to Psychological Research Data: An Introduction and Tutorial

07/11/2019
by   Marjolein Fokkema, et al.
0

Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive accuracy and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then [prediction]. As a result, PREs are often much less complex than full decision tree ensembles, while they have been found to provide similar predictive accuracy in many situations. The current paper introduces the methodology and shows how PREs can be fitted using the R package pre through several real-data examples from psychological research. The examples also illustrate a number of features of package pre that may be particularly useful for applications in psychology: support for categorical, multivariate and count responses, application of (non-)negativity constraints, inclusion of confirmatory rules and standardized variable importance measures.

READ FULL TEXT
research
07/22/2017

pre: An R Package for Fitting Prediction Rule Ensembles

Prediction rule ensembles (PREs) are sparse collections of rules, offeri...
research
09/28/2021

Improved prediction rule ensembling through model-based data generation

Prediction rule ensembles (PRE) provide interpretable prediction models ...
research
10/12/2009

Node harvest

When choosing a suitable technique for regression and classification wit...
research
12/22/2022

Machine Learning with Probabilistic Law Discovery: A Concise Introduction

Probabilistic Law Discovery (PLD) is a logic based Machine Learning meth...
research
03/13/2022

The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest

Most representative decision tree ensemble methods have been used to exa...
research
03/10/2011

COMET: A Recipe for Learning and Using Large Ensembles on Massive Data

COMET is a single-pass MapReduce algorithm for learning on large-scale d...
research
11/15/2019

LIBRE: Learning Interpretable Boolean Rule Ensembles

We present a novel method - LIBRE - to learn an interpretable classifier...

Please sign up or login with your details

Forgot password? Click here to reset