JuliaAI / JuliaAI/MLJDecisionTreeInterface.jl
Should predict be robust to changes in column order?
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- Dominant language
- Julia
- Stars
- 9
- Forks
- 3
- PR merge metrics
- No merged PRs in 30d
Description
A quick test seemed to show that the predict method is not robust to reordering the column order, which was a bit surprising.
using DataFrames
using MLJ
import MLJDecisionTreeInterface.DecisionTreeClassifier as Tree
iris = DataFrame(load_iris());
y, X = unpack(iris, ==(:target); rng=123)
tree = Tree()
mach = machine(tree, X, y)
train, test = partition(eachindex(y), 0.7)
fit!(mach, rows=train)
# Returns false
predict(mach, X[test,[3,2,1,4]]) == predict(mach, X[test,[1,2,3,4]])
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Research direction
No implementation file or test is named. Start by running the Julia example to reproduce the differing predictions, then inspect the predict and fit! entry points to determine how reordered columns are handled; done means the expected behavior is established and verified for the shown column-order case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100