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JuliaAI · Feb 20, 2020 · bb0b3f1 · bb0b3f1
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diff --git a/__site/assets/getting-started/ensembles-3/code/ex1.jl b/__site/assets/getting-started/ensembles-3/code/ex1.jl
@@ -0,0 +1,3 @@
+# This file was generated, do not modify it. # hide
+using MLJ, PyPlot
+import Statistics
diff --git a/__site/assets/getting-started/ensembles-3/code/ex2.jl b/__site/assets/getting-started/ensembles-3/code/ex2.jl
@@ -0,0 +1,8 @@
+# This file was generated, do not modify it. # hide
+Xs = source()
+ys = source(kind=:target)
+
+atom = @load DecisionTreeRegressor
+atom.n_subfeatures = 4 # to ensure diversity among trained atomic models
+
+machines = (machine(atom, Xs, ys) for i in 1:100)
diff --git a/__site/assets/getting-started/ensembles-3/code/ex3.jl b/__site/assets/getting-started/ensembles-3/code/ex3.jl
@@ -0,0 +1,5 @@
+# This file was generated, do not modify it. # hide
+Statistics.mean(v...) = mean(v)
+Statistics.mean(v::AbstractVector{<:AbstractNode}) = node(mean, v...)
+
+yhat = mean([predict(m, Xs) for  m in machines]);
diff --git a/__site/assets/getting-started/ensembles-3/code/ex4.jl b/__site/assets/getting-started/ensembles-3/code/ex4.jl
@@ -0,0 +1,2 @@
+# This file was generated, do not modify it. # hide
+one_hundred_models = @from_network OneHundredModels(atom=atom) <= yhat
diff --git a/__site/assets/getting-started/ensembles-3/code/ex5.jl b/__site/assets/getting-started/ensembles-3/code/ex5.jl
@@ -0,0 +1,2 @@
+# This file was generated, do not modify it. # hide
+X, y = @load_boston;
diff --git a/__site/assets/getting-started/ensembles-3/code/ex6.jl b/__site/assets/getting-started/ensembles-3/code/ex6.jl
@@ -0,0 +1,18 @@
+# This file was generated, do not modify it. # hide
+r = range(atom,
+          :min_samples_split,
+          lower=2,
+          upper=100, scale=:log)
+
+mach = machine(atom, X, y)
+
+curve = learning_curve!(mach,
+                        range=r,
+                        measure=mav,
+                        resampling=CV(nfolds=9),
+                        verbosity=0)
+
+plot(curve.parameter_values, curve.measurements)
+xlabel(curve.parameter_name)
+
+savefig(joinpath(@OUTPUT, "e1.svg")) # hide
diff --git a/__site/assets/getting-started/ensembles-3/code/ex7.jl b/__site/assets/getting-started/ensembles-3/code/ex7.jl
@@ -0,0 +1,18 @@
+# This file was generated, do not modify it. # hide
+r = range(one_hundred_models,
+          :(atom.min_samples_split),
+          lower=2,
+          upper=100, scale=:log)
+
+mach = machine(one_hundred_models, X, y)
+
+curve = learning_curve!(mach,
+                        range=r,
+                        measure=mav,
+                        resampling=CV(nfolds=9),
+                        verbosity=0)
+
+plot(curve.parameter_values, curve.measurements)
+xlabel(curve.parameter_name)
+
+savefig(joinpath(@OUTPUT, "e2.svg")) # hide
diff --git a/__site/assets/getting-started/ensembles-3/code/ex8.jl b/__site/assets/getting-started/ensembles-3/code/ex8.jl
@@ -0,0 +1,2 @@
+# This file was generated, do not modify it. # hide
+PyPlot.close_figs() # hide
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		# This file was generated, do not modify it. # hide
		one_hundred_models = @from_network OneHundredModels(atom=atom) <= yhat
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		# This file was generated, do not modify it. # hide
		X, y = @load_boston;
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		@@ -0,0 +1,2 @@
		# This file was generated, do not modify it. # hide
		PyPlot.close_figs() # hide