Learning to Rank Bagging Workflows with Metalearning
autoBagging
bagged trees models
abmodel-class
abmodel
bagging method
Boosting-based pruning of models
classmajority.landmarker.correlation
classmajority.landmarker.entropy
classmajority.landmarker.interinfo
classmajority.landmarker.mutual.information
classmajority.landmarker
Retrieve names of continuous attributes (not including the target)
dstump.landmarker_d1.correlation
dstump.landmarker_d1.entropy
dstump.landmarker_d1.interinfo
dstump.landmarker_d1.mutual.information
dstump.landmarker_d1
dstump.landmarker_d2.correlation
dstump.landmarker_d2.entropy
dstump.landmarker_d2.interinfo
dstump.landmarker_d2.mutual.information
dstump.landmarker_d2
dstump.landmarker_d3.correlation
dstump.landmarker_d3.entropy
dstump.landmarker_d3.interinfo
dstump.landmarker_d3.mutual.information
dstump.landmarker_d3
get target variable
Retrieve the value of a previously computed measure
K-Nearest-ORAcle-Eliminate
lda.landmarker.correlation
majority voting
Margin Distance Minimization
nb.landmarker.correlation
nb.landmarker.entropy
nb.landmarker.interinfo
nb.landmarker.mutual.information
nb.landmarker
Overall Local Accuracy
Predicting on new data with a abmodel model
FUNCTION TO TRANSFORM DATA FRAME INTO LIST WITH GSI REQUIREMENTS
Retrieve names of symbolic attributes (not including the target)
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.