DecisionTreeRegressor fails to predict

[juliohm]

Describe the bug
The DecisionTreeRegressor fails to make predictions.

To Reproduce

using MLJ
@load DecisionTreeRegressor

X = rand(100,2)
y = rand(100)

model = DecisionTreeRegressor()

theta = MLJ.fit(model, 0, X, y)
yhat = MLJ.predict(model, theta, X)

ERROR: MethodError: no method matching apply_tree(::Tuple{DecisionTree.Node{Float64,Float64},Nothing,Nothing}, ::Array{Float64,2})
Closest candidates are:
apply_tree(::Union{Leaf{T}, Node{S,T}}, ::Array{S,2}) where {S, T} at /home/juliohm/.julia/packages/DecisionTree/nfB7O/src/classification/main.jl:149
Stacktrace:
[1] predict(::DecisionTreeRegressor, ::Tuple{DecisionTree.Node{Float64,Float64},Nothing,Nothing}, ::Array{Float64,2}) at /home/juliohm/.julia/packages/MLJModels/GgPjI/src/DecisionTree.jl:268
[2] top-level scope at none:0

Expected behavior
I would expect it to work.

Additional context
No additional context.

Versions

Julia Version 1.1.0
Commit 80516ca202 (2019-01-21 21:24 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Core(TM) i7-6500U CPU @ 2.50GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_NUM_THREADS = 2

MLJ v0.2.5

[juliohm]

@ayush-1506 @ayush1999 helped with the issue over slack. The fit method returns a tuple (fitresult, report, cache). So we need to call MLJ.predict(model, theta[1], X, y).

[ayush-1506]

It's actually (fitresult, cache, report). (Though it doesn't matter in this case.)

[ablaom]

X cannot be a matrix. it must be a table supporting the Tables.jl interface.