dstump.landmarker_d2.correlation

dataset

data.char

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

dstump.landmarker_d2.correlation function

dstump.landmarker_d2.correlation — dstump.landmarker_d2.correlation

dstump.landmarker_d2.correlation: dstump.landmarker_d2.correlation

Description

Usage

Arguments