The algorithm finds weights of continous attributes basing on their correlation with continous class attribute.
a symbolic description of a model
data to process
linear.correlation uses Pearson's correlation
rank.correlation uses Spearman's correlation
NA values are not taken into consideration.
a data.frame containing the worth of attributes in the first column and their names as row names
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library(mlbench) data(BostonHousing) d=BostonHousing[-4] # only numeric variables weights <- linear.correlation(medv~., d) print(weights) subset <- cutoff.k(weights, 3) f <- as.simple.formula(subset, "medv") print(f) weights <- rank.correlation(medv~., d) print(weights) subset <- cutoff.k(weights, 3) f <- as.simple.formula(subset, "medv") print(f)
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