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‎__site/assets/getting-started/stacking/code/ex5.jl

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@@ -6,7 +6,7 @@ function print_performance(model, data...)
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verbosity=0)
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μ = round(e.measurement[1], sigdigits=5)
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ste = round(std(e.per_fold[1])/sqrt(8), digits=5)
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println("\n $model = $μ ± $(2*ste)")
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println("$model = $μ ± $(2*ste)")
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end;
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X, y = @load_boston

‎__site/assets/getting-started/stacking/code/output/ex12.res

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@@ -4,4 +4,4 @@ KNNRegressor(
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metric = Distances.Euclidean(0.0),
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leafsize = 10,
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reorder = true,
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weights = :uniform) @ 4…96
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weights = :uniform) @ 4…80

‎__site/assets/getting-started/stacking/code/output/ex13.res

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@@ -1,3 +1,3 @@
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LinearRegressor(
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fit_intercept = true,
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solver = nothing) @ 1…16
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solver = nothing) @ 6…61

‎__site/assets/getting-started/stacking/code/output/ex14.res

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@@ -1 +1 @@
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Source{:target} @ 1…02
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Source{:target} @ 1…22

‎__site/assets/getting-started/stacking/code/output/ex16.res

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@@ -1 +1 @@
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NodalMachine @ 1…44 = machine(LinearRegressor @ 1…16, 1…96, 6…97)
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NodalMachine @ 1…28 = machine(LinearRegressor @ 6…61, 1…74, 1…27)

‎__site/assets/getting-started/stacking/code/output/ex22.res

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@@ -1 +1 @@
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NodalMachine @ 1…92 = machine(LinearRegressor @ 1…16, 1…20, 1…02)
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NodalMachine @ 1…13 = machine(LinearRegressor @ 6…61, 1…03, 1…22)

‎__site/assets/getting-started/stacking/code/output/ex23.res

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@@ -1 +1 @@
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NodalMachine @ 8…37 = machine(KNNRegressor @ 4…96, 6…01, 1…02)
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NodalMachine @ 1…96 = machine(KNNRegressor @ 4…80, 1…41, 1…22)

‎__site/assets/getting-started/stacking/code/output/ex24.res

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@@ -1 +1 @@
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Node @ 6…09 = predict(1…92, table(hcat(predict(1…90, 6…01), predict(8…37, 6…01))))
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Node @ 4…34 = predict(1…13, table(hcat(predict(1…38, 1…41), predict(1…96, 1…41))))

‎__site/assets/getting-started/stacking/code/output/ex27.res

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@@ -11,4 +11,4 @@ MyTwoModelStack(
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weights = :uniform),
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judge = LinearRegressor(
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fit_intercept = true,
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solver = nothing)) @ 9…87
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solver = nothing)) @ 3…75

‎__site/assets/getting-started/stacking/code/output/ex3.res

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@@ -1 +1 @@
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Node @ 1…35 = +(#80(predict(1…17, 6…94)), #80(predict(1…30, 6…94)))
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Node @ 1…60 = +(#80(predict(6…61, 1…98)), #80(predict(1…89, 1…98)))

‎__site/assets/getting-started/stacking/code/output/ex34.out

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RandomForestRegressor @ 2…74 = 0.3621 ± 0.02018
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RidgeRegressor @ 1…05 = 0.33029 ± 0.01908
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MyAverageTwo @ 1…00 = 0.34723 ± 0.01402
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MyTwoModelStack @ 3…21 = 0.33028 ± 0.01938
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RandomForestRegressor @ 1…52 = 0.36153 ± 0.0196
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RidgeRegressor @ 4…61 = 0.33029 ± 0.01908
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MyAverageTwo @ 5…08 = 0.34554 ± 0.01264
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MyTwoModelStack @ 3…29 = 0.33028 ± 0.01938

‎__site/assets/getting-started/stacking/code/output/ex36.out

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@@ -1,2 +1 @@
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MyTwoModelStack @ 1…84 = 0.32861 ± 0.01914
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MyTwoModelStack @ 4…11 = 0.32861 ± 0.01914

‎__site/assets/getting-started/stacking/code/output/ex4.res

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@@ -8,4 +8,4 @@ MyAverageTwo(
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metric = Distances.Euclidean(0.0),
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leafsize = 10,
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reorder = true,
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weights = :uniform)) @ 3…65
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weights = :uniform)) @ 9…84

‎__site/assets/getting-started/stacking/code/output/ex5.out

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LinearRegressor @ 1…16 = 4.9779 ± 0.90056
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KNNRegressor @ 4…96 = 6.3794 ± 0.89744
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MyAverageTwo @ 3…65 = 4.9802 ± 0.94746
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LinearRegressor @ 6…61 = 4.9779 ± 0.90056
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KNNRegressor @ 4…80 = 6.3794 ± 0.89744
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MyAverageTwo @ 9…84 = 4.9802 ± 0.94746

‎__site/assets/getting-started/stacking/code/output/s1.svg

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