
The algorithms select a subset from a ranked attributes.
cutoff.k(attrs, k)
cutoff.k.percent(attrs, k)
cutoff.biggest.diff(attrs)
A character vector containing selected attributes.
a data.frame containing ranks for attributes in the first column and their names as row names
a positive integer in case of cutoff.k
and a numeric between 0 and 1 in case of cutoff.k.percent
Piotr Romanski
cutoff.k
chooses k best attributes
cutoff.k.percent
chooses best k * 100% of attributes
cutoff.biggest.diff
chooses a subset of attributes which are significantly better than other.
data(iris)
weights <- information.gain(Species~., iris)
print(weights)
subset <- cutoff.k(weights, 1)
f <- as.simple.formula(subset, "Species")
print(f)
subset <- cutoff.k.percent(weights, 0.75)
f <- as.simple.formula(subset, "Species")
print(f)
subset <- cutoff.biggest.diff(weights)
f <- as.simple.formula(subset, "Species")
print(f)
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