Example 1 does not work about partialleastsquaresregressor.jl HOT 4 CLOSED

using MLJBase, RDatasets, MLJModels
PLSRegressor = @load PLSRegressor pkg=PartialLeastSquaresRegressor

# loading data and selecting some features
data = dataset("datasets", "longley")[:, 2:5]

# unpacking the target
y, X = unpack(data, ==(:GNP))

# loading the model
regressor = PLSRegressor(n_factors=2)

# building a pipeline with scaling on data
pipe = Standardizer |> regressor
model = TransformedTargetModel(pipe, transformer=Standardizer())

# a simple hould out
(Xtrain, Xtest), (ytrain, ytest) = partition((X, y), 0.7, rng=123, multi=true)

mach = machine(model, Xtest, ytest)

fit!(mach)
yhat = predict(mach, Xtest)

mae(yhat, ytest) |> mean