Discriminate Analysis versus Random Forests on Qualitative data: Contingent Valuation Method applied to Seine Estuary wetlands

Abstract

Contingent valuation method is a technique used to quantify the value of non-market resources such as environmental features. A Contingent Valuation survey was carried out to establish the Willingness To Pay (W.T.P.) of local populations for the conservation of the Seine Estuary Wetlands, an important and threatened ecological area in Northern France. Our objective was to build a model that predicts the WTP using known samples of the same survey. As the predictors were mostly qualitative variables (nominal or ordinal), we applied a procedure to transform them into quantitative variables. We analyzed the multiple correspondences of the predictors i.e. of the correspondences of the complete binary table. The p selected explanatory variables X1 , X2 ,…, Xp were replaced by the co-ordinates on q factorial axes (with q ≤ p ) with weighting to preserve the importance of the components.

Two methods of classification were implemented: Discriminant Analysis and Random Forests, with the aim of comparing their performances in terms of classification. Our results for this survey show that Discriminant Analysis gives better predictions than Random Forests.

This deserves mention because in previous comparative studies (with methods other than Discriminant Analysis), Random Forests had consistently been shown to be superior to the other methods. These results need further investigation.