Description Usage Arguments Details Value Author(s) Examples
The algorithms select a subset from a ranked attributes.
1 2 3 | cutoff.k(attrs, k)
cutoff.k.percent(attrs, k)
cutoff.biggest.diff(attrs)
|
attrs |
a data.frame containing ranks for attributes in the first column and their names as row names |
k |
a positive integer in case of |
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.
A character vector containing selected attributes.
Piotr Romanski
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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)
|
OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed
attr_importance
Sepal.Length 0.4521286
Sepal.Width 0.2672750
Petal.Length 0.9402853
Petal.Width 0.9554360
Species ~ Petal.Width
<environment: 0x45b6b78>
Species ~ Petal.Width + Petal.Length + Sepal.Length
<environment: 0x45d54d0>
Species ~ Petal.Width + Petal.Length
<environment: 0x45f6fd0>
Warning message:
system call failed: Cannot allocate memory
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