abmodel-class

abmodel is an S4 class that contains the ensemble model.
Besides the base learning algorithms--<code>base_models</code> --
abmodel class contains information about the
dynamic selection method to apply in new data.

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

abmodel-class function

<dl class="dl-horizontal">
<dt><code>base_models</code></dt><dd>a list of decision tree classifiers</dd></dl><dt><code>form</code></dt><dd>formula</dd><dt><code>data</code></dt><dd>dataset used to train <code>base_models</code></dd><dt><code>dynamic_selection</code></dt><dd>the dynamic selection/combination method
to use to aggregate predictions. If <code>none</code>, majority vote is used.</dd>

abmodel-class: abmodel-class

Description

Arguments

Slots

See Also