update glm tutorial (#178)

Author: OkonSamuel

_literate/EX-GLM/Manifest.toml

@@ -133,9 +133,9 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 
 [[deps.Distributions]]
 deps = ["ChainRulesCore", "DensityInterface", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Test"]
-git-tree-sha1 = "6a8dc9f82e5ce28279b6e3e2cea9421154f5bd0d"
+git-tree-sha1 = "97e9e9d0b8303bae296f3bdd1c2b0065dcb7e7ef"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.37"
+version = "0.25.38"
 
 [[deps.DocStringExtensions]]
 deps = ["LibGit2"]

_literate/EX-GLM/tutorial.jl

@@ -61,9 +61,11 @@ y = copy(dfY1)
 coerce!(X, autotype(X, :string_to_multiclass))
 yv = Vector(y[:, 1])
 
-LinearRegressorPipe = @pipeline(Standardizer(),
-                                OneHotEncoder(drop_last = true),
-                                LinearRegressor())
+LinearRegressorPipe = Pipeline(
+    Standardizer(),
+    OneHotEncoder(drop_last = true),
+    LinearRegressor()
+)
 
 LinearModel = machine(LinearRegressorPipe, X, yv)
 fit!(LinearModel)
@@ -73,7 +75,7 @@ fp = fitted_params(LinearModel)
 #
 # We can quickly read the results of our models in MLJ.  Remember to compute the accuracy of the linear model.
 
-ŷ = MLJ.predict(LinearModel, Xm)
+ŷ = MLJ.predict(LinearModel, X)
 yhatResponse = [ŷ[i,1].μ for i in 1:nrow(y)]
 residuals = y .- yhatResponse
 r = report(LinearModel)
@@ -99,9 +101,11 @@ coerce!(X, autotype(X, :string_to_multiclass))
 yc = CategoricalArray(y[:, 1])
 yc = coerce(yc, OrderedFactor)
 
-LinearBinaryClassifierPipe = @pipeline(Standardizer(),
-                                       OneHotEncoder(drop_last = true),
-                                       LinearBinaryClassifier())
+LinearBinaryClassifierPipe = Pipeline(
+    Standardizer(),
+    OneHotEncoder(drop_last = true),
+    LinearBinaryClassifier()
+)
 
 LogisticModel = machine(LinearBinaryClassifierPipe, X, yc)
 fit!(LogisticModel)