Agnostic Learning vs. Prior Knowledge

The challenge is now over. But it remains open for post-challenge submissions!

Data Grid (CMA)

Submitted by Marc B oulle

Data Grid (CMA)

Prior submission (prior for ada, gina and sylva, agnostic for hiva and nova)

Data Grids extend the MODL discretization and value grouping methods to the multivariate case (paper submitted to IJCNN 2007).
An ensemble of data grid is averaged according to the compression-based averaging schema (CMA) (see SNB(CMA) submission)

Preprocessing for sylva: the 80 soil_type binary variables are merged into two categorical variables


BER guess:
ada: 0.192
gina: 0.140
hiva: 0.310 (agnostic)
nova: 0.135 (agnostic)
sylva: 0.008

Dataset	Balanced Error			Area Under Curve
	Train	Valid	Test	Train	Valid	Test
ada	0.1734	0.1994	0.1756	0.8507	0.8045	0.8464	prior
gina	0.1052	0.089	0.1254	0.9588	0.972	0.9479	prior
hiva	0.2651	0.3276	0.3242	0.7659	0.7647	0.717	agnostic
nova	0.1094	0.096	0.1229	0.9226	0.9338	0.9159	agnostic
sylva	0.0205	0.009	0.0228	0.981	0.9838	0.9798	prior
Overall	0.1347	0.1442	0.1542	0.8958	0.8917	0.8814	prior

This entry is a complete prior knowledge entry.

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