mdsq

form

preds

data

<p>ratio of the total number of models to cut off</p>

cutPoint

A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.

Vitor Cerqueira

autoBagging

Learning to Rank Bagging Workflows with Metalearning

Fabio Pinto

Carlos Soares

Joao Mendes-Moreira

mdsq function

Margin Distance Minimization — mdsq

Margin Distance Minimization

mdsq: Margin Distance Minimization

Description

Usage

Arguments