Produces a heatmap that allows to identify what observations are covered by the most important decision rules. Details can be found in Nalenz & Villani (2017).
list containing a model of class "HorseRuleFit".
number of most important rules to be shown in the RuleHeat plot. library(MASS) data(Boston) # Split in train and test data N = nrow(Boston) train = sample(1:N, 400) Xtrain = Boston[train,-14] ytrain = Boston[train, 14] Xtest = Boston[-train, -14] ytest = Boston[-train, 14]
hrres = HorseRuleFit(X = Xtrain, y=ytrain, thin=1, niter=200, burnin=10, L=5, S=6, ensemble = "both", mix=0.3, ntree=100, intercept=FALSE, linterms=1:13, ytransform = "log", alpha=1, beta=2, linp = 1, restricted = 0)
#Create a ruleheat plot. ruleheat(hrres = 10)
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