diff --git a/examples/boston/notebook.ipynb b/examples/boston/notebook.ipynb
index 61663d3d..d653cbc3 100644
--- a/examples/boston/notebook.ipynb
+++ b/examples/boston/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -124,7 +124,7 @@
        "AbstractVector{Continuous} (alias for AbstractArray{Continuous, 1})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -159,7 +159,7 @@
        "└─────────┴───────────────┴──────────────────────────────────┘\n"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -228,7 +228,7 @@
        "GenericBuilder{#1#2}\n"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -251,21 +251,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: For silent loading, specify `verbosity=0`. \n",
-      "└ @ Main /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/loading.jl:168\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "[ Info: For silent loading, specify `verbosity=0`. \n",
       "import MLJFlux ✔\n"
      ]
     },
@@ -274,7 +267,7 @@
       "text/plain": [
        "NeuralNetworkRegressor(\n",
        "    builder = GenericBuilder(\n",
-       "            apply = var\"#1#2\"()),\n",
+       "            apply = Main.##283.var\"#1#2\"()),\n",
        "    optimiser = ADAM(0.001, (0.9, 0.999), IdDict{Any, Any}()),\n",
        "    loss = Flux.Losses.mse,\n",
        "    epochs = 20,\n",
@@ -286,7 +279,7 @@
        "    acceleration = CPU1{Nothing}(nothing))"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -334,7 +327,7 @@
        "    cache = true)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -370,89 +363,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "┌ Info: Training Machine{DeterministicPipeline{NamedTuple{,…},…},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Training Machine{Standardizer,…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Features standarized: \n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:825\n",
-      "┌ Info:   :CRIM    mu=3.689791468926554  sigma=8.486186608186063\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :ZN    mu=10.392655367231638  sigma=22.078531026900794\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :INDUS    mu=11.477372881355933  sigma=6.83192917494762\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :NOX    mu=0.5619451977401131  sigma=0.11706875291419269\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :RM    mu=6.256881355932204  sigma=0.655703581671515\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :AGE    mu=69.98474576271187  sigma=27.70464405221309\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :DIS    mu=3.704779096045197  sigma=2.018034269570148\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :RAD    mu=5.72316384180791  sigma=2.4534591735797426\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :TAX    mu=415.97457627118644  sigma=172.0508915025351\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :PTRATIO    mu=18.54971751412429  sigma=2.176727910395866\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :B    mu=354.88437853107337  sigma=91.70747435121616\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info:   :LSTAT    mu=12.997796610169493  sigma=7.067143799083826\n",
-      "└ @ MLJModels /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/builtins/Transformers.jl:836\n",
-      "┌ Info: Training Machine{TransformedTargetModelDeterministic{NeuralNetworkRegressor{GenericBuilder{#1#2},…}},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Training Machine{Standardizer,…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Training Machine{NeuralNetworkRegressor{GenericBuilder{#1#2},…},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Loss is 0.3687\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.2216\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.1775\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.1519\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.1322\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.117\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.1064\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.09521\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.08949\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.08277\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.07997\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.07727\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.0719\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.07051\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.06697\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.06423\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.06024\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.05974\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.05666\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n",
-      "┌ Info: Loss is 0.05577\n",
-      "└ @ MLJFlux /Users/anthony/.julia/packages/MLJFlux/ex3rh/src/core.jl:127\n"
+      "[ Info: Training Machine{DeterministicPipeline{NamedTuple{,…},…},…}.\n",
+      "[ Info: Training Machine{Standardizer,…}.\n",
+      "[ Info: Features standarized: \n",
+      "[ Info:   :CRIM    mu=3.689791468926554  sigma=8.486186608186063\n",
+      "[ Info:   :ZN    mu=10.392655367231638  sigma=22.078531026900794\n",
+      "[ Info:   :INDUS    mu=11.477372881355933  sigma=6.83192917494762\n",
+      "[ Info:   :NOX    mu=0.5619451977401131  sigma=0.11706875291419269\n",
+      "[ Info:   :RM    mu=6.256881355932204  sigma=0.655703581671515\n",
+      "[ Info:   :AGE    mu=69.98474576271187  sigma=27.70464405221309\n",
+      "[ Info:   :DIS    mu=3.704779096045197  sigma=2.018034269570148\n",
+      "[ Info:   :RAD    mu=5.72316384180791  sigma=2.4534591735797426\n",
+      "[ Info:   :TAX    mu=415.97457627118644  sigma=172.0508915025351\n",
+      "[ Info:   :PTRATIO    mu=18.54971751412429  sigma=2.176727910395866\n",
+      "[ Info:   :B    mu=354.88437853107337  sigma=91.70747435121616\n",
+      "[ Info:   :LSTAT    mu=12.997796610169493  sigma=7.067143799083826\n",
+      "[ Info: Training Machine{TransformedTargetModelDeterministic{NeuralNetworkRegressor{GenericBuilder{#1#2},…}},…}.\n",
+      "[ Info: Training Machine{Standardizer,…}.\n",
+      "[ Info: Training Machine{NeuralNetworkRegressor{GenericBuilder{#1#2},…},…}.\n",
+      "[ Info: Loss is 0.3687\n",
+      "[ Info: Loss is 0.2216\n",
+      "[ Info: Loss is 0.1775\n",
+      "[ Info: Loss is 0.1519\n",
+      "[ Info: Loss is 0.1322\n",
+      "[ Info: Loss is 0.117\n",
+      "[ Info: Loss is 0.1064\n",
+      "[ Info: Loss is 0.09521\n",
+      "[ Info: Loss is 0.08949\n",
+      "[ Info: Loss is 0.08277\n",
+      "[ Info: Loss is 0.07997\n",
+      "[ Info: Loss is 0.07727\n",
+      "[ Info: Loss is 0.0719\n",
+      "[ Info: Loss is 0.07051\n",
+      "[ Info: Loss is 0.06697\n",
+      "[ Info: Loss is 0.06423\n",
+      "[ Info: Loss is 0.06024\n",
+      "[ Info: Loss is 0.05974\n",
+      "[ Info: Loss is 0.05666\n",
+      "[ Info: Loss is 0.05577\n"
      ]
     },
     {
@@ -461,11 +416,11 @@
        "Machine{DeterministicPipeline{NamedTuple{,…},…},…} trained 1 time; caches data\n",
        "  model: MLJBase.DeterministicPipeline{NamedTuple{(:standardizer, :transformed_target_model_deterministic), Tuple{Unsupervised, Deterministic}}, MLJModelInterface.predict}\n",
        "  args: \n",
-       "    1:\tSource @768 ⏎ `Table{AbstractVector{Continuous}}`\n",
-       "    2:\tSource @147 ⏎ `AbstractVector{Continuous}`\n"
+       "    1:\tSource @185 ⏎ `Table{AbstractVector{Continuous}}`\n",
+       "    2:\tSource @132 ⏎ `AbstractVector{Continuous}`\n"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -485,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +470,7 @@
        " 0.055767894928594526"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -533,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -547,7 +502,7 @@
        " 0.05"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -579,158 +534,307 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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+      ],
+      "text/plain": [
+       "Plot{Plots.GRBackend() n=5}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -764,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -773,7 +877,7 @@
        "0.0001"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -817,7 +921,7 @@
        "clear (generic function with 1 method)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,7 +944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -849,7 +953,7 @@
        "update_epochs (generic function with 1 method)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -873,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -884,12 +988,12 @@
        " NumberSinceBest(6)\n",
        " InvalidValue()\n",
        " TimeLimit(Dates.Millisecond(60000))\n",
-       " WithLossDo{typeof(update_loss)}(update_loss, false, nothing)\n",
-       " WithReportDo{typeof(update_training_loss)}(update_training_loss, false, nothing)\n",
-       " WithIterationsDo{typeof(update_epochs)}(update_epochs, false, nothing)"
+       " WithLossDo{typeof(Main.##283.update_loss)}(Main.##283.update_loss, false, nothing)\n",
+       " WithReportDo{typeof(Main.##283.update_training_loss)}(Main.##283.update_training_loss, false, nothing)\n",
+       " WithIterationsDo{typeof(Main.##283.update_epochs)}(Main.##283.update_epochs, false, nothing)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -914,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +1029,7 @@
        "            standardizer = Standardizer,\n",
        "            transformed_target_model_deterministic = TransformedTargetModelDeterministic{NeuralNetworkRegressor{GenericBuilder{#1#2},…}},\n",
        "            cache = true),\n",
-       "    controls = Any[Step(1), NumberSinceBest(6), InvalidValue(), TimeLimit(Dates.Millisecond(60000)), WithLossDo{typeof(update_loss)}(update_loss, false, nothing), WithReportDo{typeof(update_training_loss)}(update_training_loss, false, nothing), WithIterationsDo{typeof(update_epochs)}(update_epochs, false, nothing)],\n",
+       "    controls = Any[Step(1), NumberSinceBest(6), InvalidValue(), TimeLimit(Dates.Millisecond(60000)), WithLossDo{typeof(Main.##283.update_loss)}(Main.##283.update_loss, false, nothing), WithReportDo{typeof(Main.##283.update_training_loss)}(Main.##283.update_training_loss, false, nothing), WithIterationsDo{typeof(Main.##283.update_epochs)}(Main.##283.update_epochs, false, nothing)],\n",
        "    resampling = Holdout(\n",
        "            fraction_train = 0.8,\n",
        "            shuffle = false,\n",
@@ -936,11 +1040,11 @@
        "    operation = MLJModelInterface.predict,\n",
        "    retrain = false,\n",
        "    check_measure = true,\n",
-       "    iteration_parameter = :(neural_network_regressor.epochs),\n",
+       "    iteration_parameter = nothing,\n",
        "    cache = true)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -950,7 +1054,6 @@
     "    IteratedModel(model=pipe,\n",
     "                  controls=controls,\n",
     "                  resampling=Holdout(fraction_train=0.8),\n",
-    "                  iteration_parameter=:(neural_network_regressor.epochs),\n",
     "                  measure = l2)"
    ]
   },
@@ -963,23 +1066,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "┌ Info: Training Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: final loss: 18.029846309038444\n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/train.jl:44\n",
-      "┌ Info: final training loss: 0.15870439154333713\n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/train.jl:46\n",
-      "┌ Info: Stop triggered by NumberSinceBest(6) stopping criterion. \n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/stopping_controls.jl:54\n",
-      "┌ Info: Total of 13 iterations. \n",
-      "└ @ MLJIteration /Users/anthony/.julia/packages/MLJIteration/jtWxG/src/core.jl:35\n"
+      "[ Info: Training Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…}.\n",
+      "[ Info: final loss: 18.029846309038444\n",
+      "[ Info: final training loss: 0.15870439154333713\n",
+      "[ Info: Stop triggered by NumberSinceBest(6) stopping criterion. \n",
+      "[ Info: Total of 13 iterations. \n"
      ]
     },
     {
@@ -988,11 +1086,11 @@
        "Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…} trained 1 time; does not cache data\n",
        "  model: MLJIteration.DeterministicIteratedModel{MLJBase.DeterministicPipeline{NamedTuple{(:standardizer, :transformed_target_model_deterministic), Tuple{Unsupervised, Deterministic}}, MLJModelInterface.predict}}\n",
        "  args: \n",
-       "    1:\tSource @623 ⏎ `Table{AbstractVector{Continuous}}`\n",
-       "    2:\tSource @109 ⏎ `AbstractVector{Continuous}`\n"
+       "    1:\tSource @189 ⏎ `Table{AbstractVector{Continuous}}`\n",
+       "    2:\tSource @474 ⏎ `AbstractVector{Continuous}`\n"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1012,177 +1110,345 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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W4fjx4xctWsQ6UeyOHTtcXV2VFBgAAEBFSp9irbS0VCqV1q5dmxBSv359xT1Sa2vrqqaYu379+qlTp5jJewAAAJRKiVWjCxYsOHfu3MOHD5cuXdqoUaOKT2VkZKxdu5b1prCFhcXevXtLSkquXbu2fPnyigWKlWRnZy9cuFDxcNKkSR9dkQu38tSfXC7//El4JRLJF5/JV+spKmVUHYiGqdmHDe9zzUil0k96t6saHFmJEhNh165dfX194+Li5s6d26FDBzs7O2Z/YWFhjx49evfuXWkYACMhIYHJZ+fOnevYsWO3bt2srKxYzy+XyxVz2ZHq/cH48Kk/uVxezc/uB8hkss8/ia6RyWTMhP2qDkTD4MPGpU99t+VyeXW+9pWYCBs2bNiwYcOwsLC7d+/u3r17+vTphJDi4uKuXbu6ublFRUWxvkrRqgsKCjIzM3v48GFVidDS0nLZsmWfFBKPx7ty5QrSodp68OCBo6MjM57yc0gkks8/iQ5iZoNUdRQapmYfNoqizp49m5ycrISItFZ+fr6+vv4nvdtCobA6v+2U/qGnaTojI4OZSq68vLxPnz61a9fetGlTxeDy8/MFAgFTKarw/PnzrKwse3v7LxjM5MmTly9f/ue9Fxb6pK6RZvzylclkH73lq03eXz0RQPvMmjVr586dt2q0WIQWqNnXWuvWra2trZURj1IS4ZMnT0aPHh0QECAUCs+dO5eXl9e/f39CyJQpU86ePfvNN9+MGTOGEFK3bt3Zs2cTQvr37+/j4zN//vxTp06tX7/e19e3tLR0586dw4cPd/pCc8gy/Pz8Dhw4sOCOvFBCL2quAdmFpuni4mJMyAKgZTp16tSppvOCaoGCggJmRVw1oZRE6OjoOHHixNu3b5eXl3/77be9e/dmGrNdu3b19PRUHKaYtWvChAnM4PoWLVowy3wYGhpu3749ODhYGeF5isiOp8o4MQAAaB6lJEKhUNilS5cuXbpU2h8UFBQUFPT+8e3bt2c2RCLRkCFDlBFSRV5iKj6HZYlnAADQQUofR6iGnM2o1GK6SKrqOAAAQA3oYiLkU8TVjHqci9pRAADQyURImLujSIQAAKCzidBTTMXnIBECAICuJkIvMUnIUXUQAACgBnQ1EYrQIgQAAEJ0NhE6mVFpJSgcBQAAXU2EfIq4mlOPUC8DAKDzdDQRkv8Nq0ciBADQdbqbCD1FVAJahAAAOk93E6GXmKBFCAAAupwIqXiMoAAA0Hm6mwidTKl0FI4CAOg83U2EPIo0MKceopsQAEC36W4iJCgcBQAAHU+EnmIqAYkQAEC36XQi9BKhcBQAQNfpdiIUU/G5qg4CAABUSqcToaMplVlKF0hUHQcAAKiOTidCpnAUM44CAOgynU6EBBOtAQDoPJZEmJGR4ezsfPr0ae6j4R5GUAAA6DiWRCgUCp89e2ZsbMx9NNzDjKMAADqOJRGKRCJ/f/+zZ89yHw33MOMoAICOE7DunTNnzpAhQyQSSZcuXSwsLCo+5eTkxElgHKlvQmWV0QUSYipUdSgAAMC5R48esSfCwYMHp6WlzZs3b968eZWeommtupGoKBxtXptSdSwAAMAduVzev3//a9eusSfCpUuXlpSUcByTqniJqPgcJEIAAN3y559/Xr9+/d69e+yJcNCgQRwHpEIoHAUA0EEHDx7s06ePqakpeyJkyOXyV69evXr1qm7duvXq1RMIPnSw5vISU+seylUdBQAAfAGJiYnJycnM9t27d6XSKledffHihZ+fH6mqWIYQsmrVqvnz52dmZjIPzczMJk6c+J///IfP53/JkNWAp5gkoHAUAEArXLhwYf/+/cx2RkZGnTp1qjqypKREX1+fVJUIo6KiJk6cGBAQ0Ldv37p162ZmZh49enTu3LnFxcVLlixRRugqhMJRAACtMXz48OHDhzPbMTExO3furOpIa2vrrKwswjqOUC6XL1q0aMSIEXFxcWPHju3Zs+fIkSP/+OOPWbNmrV69uqCgQEnRqwqPIm5Yqh4AQMe0aNHi0qVLhDURpqenp6enf/fdd5X2f/fdd2VlZU+ePOEiQG6hXgYAQNdERET89ddfixYtYkmEBgYGhBCmwVgRs8fIyIiD+DiGRAgAoGusrKwuXrz4/Plz9inWfH19J02a9OLFC8XOzMzMyMhIOzs7Nzc3DuPkCNagAADQQd7e3ps2bWIvllm7dm1wcLCLi4u/v7+NjU1GRsY///wjkUgOHz7M42nhyk1eYoIZRwEAdBN7VvPz87t9+/bw4cNzcnIuXryYmprar1+/mzdvdurUieP4uFHflMopo/PKVR0HAABwjqVFWFRUNG/evPDw8HXr1nEfkEpQhLiZU49yab86mGgNAEC3sLQICwsLFy9eXFZWxn00KuQlpuLRTQgAoHtYEmHt2rVtbGwSExO5j0aFPMVUAgpHAQB0D8utUR6Pt3z58hkzZri7u/v6+tbgpGVlZUeOHImPj5fL5Y0aNerZs6diYrYnT57s3buXEDJgwIAGDRq8/9rCwsKtW7e+ffu2TZs2YWFhNbh6zXiJqfNvMOMoAIDOYS+W2bZtW25ubvPmze3s7Jr9W3VOmpaWduDAAT09PVNT0zlz5gwYMIDZ//jx4xYtWkgkEqlU2qJFi0ePHlV6oVwuDwoKOnfunJWV1ahRo7jspPQSoXAUAEAXsQ+fcHBwEIlENT6pvb19dHQ0s92lSxdvb++SkhJDQ8OoqKiBAwcuWLCAEJKXlxcVFbVhw4aKLzx16lRGRsbly5cFAoG3t3dERMSoUaO4mebbwZTKLafzyom5HgdXAwAAdcGSCOVy+fTp0y0sLMzNzT//AteuXXNycmJmq7lw4cLPP//M7A8JCfnxxx8rHXzhwoWgoCBmvafAwMC0tLTExERuhvBThLiLqIe5tD8KRwEAdAlLInz79q2zs/OZM2fatWv3Oaf29/dPTk6mKCo2NpaiKEJIamqqYkUMKyurN2/eVHpJamqqjY3N/yITCCwsLN68eVNVIkxLS1NMMU4IiYqK+swVE93NeHfSZU3M1Khkhqbp0tJS7Vv6StlKS0uFQiwm8mlKS0sFAoG2LjuqPPiw1QBnb5pEIqHpj3+ls3zoxWLxFwkxNjY2PT1948aNffv2vXPnjoGBgUAgkMlkzLNSqfT9qwgEArn8/ytWJBLJByIxMDCo2Gepr6//mbPeeInpJ4VEKFSjFiFN00KhEP+bfSq8aTUgk8mQCGsAH7Ya4OxN4/P5TDPsw1g+9IaGhuHh4Vu2bPnMFqG5ubm5ufmyZcu2bdt2/fr11q1b29jYKFqBb968UTT+FGxsbJ4/f85sl5SU5Obmvn9MxfOPHj36cyKspKEFfT5eplbNL5qm+Xy+WoWkEfCm1QD/HVUHomHwptUAZ29aNVtH7L/+WrVqNXPmTH9//86dO1da3nfkyJEfPWlpaSnTKUgISUpKys/Pt7OzI4R07tw5Ojq6b9++hJDo6OjOnTszx1y+fNnT01MkEnXu3LlLly75+flmZmYxMTFubm5OTk7V+TO+CMw4CgCgg9gT4ezZs7OysrKysq5evVrpqeokwl27dq1bt65x48ZlZWWnTp2aNm2ao6MjIWT8+PH+/v7du3enKOr27dsrV65kjg8JCYmJiQkODvbz8wsMDGzdunWLFi1iYmK2bt36eX/dp7E3ofLL6dxyIkLhKACAzmBPhI8fP67YV/ephg0b5uPj8/jxY0NDwwULFjg7OzP7ra2t7927d+rUKULIzp07zczMmP1nzpzx8PBgtvfv33/hwoWXL19Onz6dy+YgqVA42hKFowAAOoM9EX7mwAk+n9+8efPmzZu//5SZmVnv3r0r7fT391dsUxTVtm3bz7n652AmWkMiBADQHVVWiEkkklOnTsXHx0ul0hkzZhBCHj16ZGRkZG9vz2F4XMNS9QAAuoY9Eb558yYsLOz+/fv6+vqWlpZMIty8efOVK1cuX77MbYSc8hJRp19hxlEAAB3CXlo6YsSIkpKS69evnzx5UrGzX79+V69ezcvL4yo2FfASk/hcVQcBAAAcYl+PMDY2NioqqlmzZhWHIrq4uMjl8pcvX3IYHtfqmVAF5XQulqoHANAZLIkwPz9fJpO9X7EplUoJIeXl2pwlFIWjqg4EAAA4wr4wr7Gx8fsjCE+fPi0QCFxcXDgJTGVQLwMAoFNYimWEQuHAgQOnTZtmY2Ojp6dHCJHL5SdOnJg0aVLv3r0Vg/+0FRIhAIBOYa8aXb58+ZMnT0JDQw0MDKRSqUgkKigoaNKkyZo1aziOj3teYioWhaMAADqDPRGamJicPXv22LFjzEq5JiYmgYGB4eHhTANRu3mKSAIKRwEAdEaVA+p5PF63bt26devGZTTqoJ4JVSihc8qIWF/VoQAAgPJ91gJ+WgmFowAAOgWJkIWXCPUyAAC6AomQhZeYikeLEABANyARsmDWoFB1FAAAwAUkQhZYqh4AQHdUKxHev39/586dd+/eVXY0asLOmCqS0tllqo4DAACUjz0RhoSEzJw5k9k+evSoj4/P0KFDmzZtun37dg5jUxmKEA8UjgIA6AaWRCiRSM6fP9+uXTvm4axZs/z8/JKSkn744Yfp06dLJBJuI1QNTLQGAKAjWBJhVlaWVCp1cHAghLx+/frOnTs//PCDk5PTlClT0tLSUlJSOA9SBTxFVAJahAAAOoAlERoZGRFCCgsLCSHHjh3j8XiBgYGEEGNjY0KIdi/Mq4AWIQCAjmBJhGZmZs7OzitXrnz48OGmTZv8/f1r1apFCHn27BkhxMrKiusYVcFLTJAIAQB0Aftco8uWLQsPD9+xY4eBgcEff/zB7Dx8+LCNjY2dnR2H4amMnTFVIiXZZaQWZhwFANBq7Imwe/fuSUlJDx488Pb2trW1ZXY2atRo8+bNHMamYh4iKiGH/tqaUnUgAACgFDk5Ofv27aty9QlbW1tFCmR07dpV+VGpEWaiNSRCAACtVFhY6Ofn5+PjU+WA+gcPHowePbp169atWrVi9mzZsuXgwYNcRah6mGgNAECL7d2719LSct++feyJ8OzZs82bNz916pRQKHzx4gWzs7i4eOrUqRwGqWIoHAUA0GJnzpzp2LEjRVHst0YjIyM7dOiwb9++y5cvf/PNN8zOkJCQ77//PjU1tW7duhyGqjJeIhSOAgBomD///DMuLo7Zfvr0aWlpaVVHvnnzJiQkhLAWy2RmZj569GjXrl16enoU9f89ZEy9qO4kQltjqkxOMkuJpYGqQwEAgOoxNDQUi8XMtrGxcUlJSVVH8ng8mUxGWBOhXC4nhAgElZ9KT08nhOjr69B4AmbG0daolwEA0BCBgYHMJDCEkJiYmJ07d1Z1pI2Nzdu3bwnrgPratWvb2dlFR0cTQiq2CLdu3SoWi93c3L5s0OrME0vVAwBoqQ4dOhw7dkwul7O0CCmKmjFjRmRkZH5+vqOjo1QqPXfu3P79+7ds2bJgwYL3W4pazEuMGUcBALRT3759o6KiOnbsyJ7Vvvvuu6Kiojlz5jAzjrZr105PT2/KlClTpkzhNk4V8xJTx17IVR0FAAB8eYaGhn///fexY8dYEiFN07m5uZGRkSNHjrx8+XJqamqtWrVatmxZp04d7gNVLcw4CgCgxYyMjPr168eSCN++fWtjY3Py5MnQ0NCwsDDuI1MfNkaUBIWjAABajaVYxtzcnM/n61R16Ad4YGFCAACtxr4eYY8ePfbs2cN9NGoIE60BAGg39mKZLl26/PDDD69everSpUulrsE+ffpwEpi68MIICgAArcaeCKdMmZKZmRkbGxsbG1vpKZrWrazgJaaOpKBwFABAa7EnwqtXrzITzwCzGJOqowAAAGVhT4QODg4cx6G26hoRKQpHAQC0l1KmiSkvL4+Ojj537lx2dra3t/f48eMtLS0JISdOnLh48WLFIxcuXMjj/atgZ+7cucXFxcx2o0aNBgwYoIwIPwlTOBqAGUcBALRRlYnwt99+279/f1JSkoV9l1cAACAASURBVCItMZKSkj560tevX2/atKl///42Njbbt28PDAy8deuWnp6ehYWFk5MTc8wff/yRmppaKQsSQlavXj1hwgSmQkdNhvAzCxMiEQIAaCX2RLh8+fLJkye3bdu2vLzc3Nzc2dn54sWLRUVF4eHh1Tmpo6PjhQsXmO2QkBAzM7P4+HgfHx8/Pz8/Pz9m/4YNG0aMGMH68n79+rm6un7yn6I0WKEXAECLsa9Qv3z58gkTJpw7d65Vq1YdO3Y8ePBgYmLi119//X4D7qNSU1NlMpm1tXXFnbdu3Xr06FFVaXXRokWRkZG7du1Sk4IdJEIAAC3G0iLMy8tLTU0dPHgwIYSiKGZ5X1NT01WrVnl5eS1cuJDp8KsOqVQ6bNiwMWPGVFrLd9u2bb169VKsnVjRgAEDvL29S0tLf/7556NHjzKrQbF69epVUFCQ4uGhQ4eUtDKGg5CKzxEy849zjKbpkpISXRuy8vmKiooqriAG1VFaWioQCHRqeZkvAh+2GuDsTSstLa3O92eVC/MyU6zVqVPn1atXzP569erJZLKUlJRqJkKZTDZkyBB9ff1ly5ZVimzv3r2///4766tWr17NbPTp08fe3v7Ro0fu7u6sR9aqVWvGjBmKh2ZmZjVosFaHiwmR0ZISgUltzgtHaZrm8XjGxsZcX1jD0TRtYmKi6ig0jOAdVQeiYfBhqwHO3jQDA4PqZFyWD71YLLawsEhMTPT09GzYsOHWrVvfvHljY2PDTLpma2tbncvL5fIRI0akpaUdO3ZMT0+v4lOHDh0SiURt2rT58Bnq1q1rbm6elpZWVSI0MjIKDg6uTjCfj5lorU1d/O4DANA27E2o0NDQ3377jRDSu3dvMzMzZ2dnR0fH0aNH9+vXr1JvHyuapiMjI588eRITE2NoaFjp2W3btg0bNqxi6y02NvbGjRuEkOzs7KKiImZndHR0aWlpw4YNa/aHfVmYaA0AQFux3wb59ddfmQ19ff2///57586dz58/9/HxGTZsWHVOeu/evfXr15uYmNjb2zN79u7dy6zolJycHBcXt3379orHr1692sfHp1mzZrdu3erVq1eDBg1KS0vfvn27Y8eOWrVq1fyP+3IwvwwAgLb6eH+AtbX11KlTP+mk3t7e2dnZFfcobgfb2NhkZmaam5tXfHbfvn1Mz0RwcHBycnJSUpKBgYGrq6v6LAXlKaYOJWPGUQAALaSUjnE+n89aEUoI0dPTq9RlSAgxNTVVbIvF4mbNmikjqs/hJaYe4NYoAIA2Yk+Efn5+mZmZrE9VZ2YZ7WNtSAghGaWE+8JRAABQqirXI6w4bC4jI+Ovv/7KyMioZh+hVvIQUfE5dCAKRwEAtAt7Ipw5c2alPVKpdOjQofn5+coPSU0x88sgEQIAaJnqjkAXCAQzZ87csmVLWlqaUgNSW54iKgGFowAAWucTpmIxNjaWy+Vv3rxRXjTqDDOOAgBopeomwtzc3J9++kkgEKjVuhBcQiIEANBK1aoaLSkpefv2LUVRc+bM0dlZ9awMCSEkvYTUqTxVDgAAaLBqVY0SQqytrYODg729vTmJSk15iqj4XLqOIeplAAC0R3WrRoG8uzvaFoWjAABaRCnrFmkrZg0KVUcBAABfEnuL8Mcff/zokMFBgwZ9/fXXSghJfXmJqYPPMOMoAIBWYU+EFy9efPjwYUFBAUVRtWrVys3NlclkRkZGFddgateuHVdBqgssxgQAoH3Yb43OnDnT3Nx83759paWlmZmZpaWlx48ft7GxmTFjRtI7ffv25ThWlatjSHgUSStRdRwAAPDlsLQI5XL5mDFjVqxY0adPn/8dJBB07NjR3Nw8JCSkX79+OjuCghDiKabic2grFI4CAGgLlhZhenr6q1evGjVqVGl/o0aNiouLnzx5wklgagoTrQEAaBmWRGhqaioUCo8fP15p/4kTJwghFhYWXMSlrjC/DACAlmG5NWpsbDxkyJCpU6e+fPmyR48edevWTU9PP3ny5MqVKzt27Ojg4MB9lOrDS0ztR+EoAIAWYa8a/eWXX4RC4dq1a6Oiopg9PB6vX79+GzZs4DA2deSNpeoBALQLeyLU19dft27dnDlzHjx48Pr1a2tra09PTxsbG46DU0OWBkRAkbcl/1uzHgAANB17ImTUrl27bdu2nIWiKZj5ZaxROAoAoBXYxxFGR0cfOXKE2c7JyRkwYICrq2t4eHhWVhaHsakp1MsAAGiHK1eu9O7dmz0Rfv/99xkZGcz2tGnTDh061Lx580uXLg0dOpS7ANUV5pcBANACr169CgsLCw4OZkmE+fn5b968ad68OSFEIpHs379/6tSpe/fu/f33348fP56dnc15tOrFS0zFYyghAICG27p1a/v27UePHs2SCIuKiggh5ubmhJCrV6/m5eV169aNENK0aVOaplNSUjiOVd3g1igAgBa4fft2y5YtCWuxTO3atYVC4f379+vXr3/gwAFLS8vGjRsTQpi2oIGBAcexqhsUjgIAqK01a9bs3LmT2c7NzXV0dKzqyLdv34rFYsKaCAUCQa9evYYPHx4SEhIdHT1y5Eg+n08IuXXrllAotLe3V07wmoRpFKJwFABA3fTs2bNVq1bM9oULF+Li4qo60tTUtKSkhFRVNbpx48aePXs+ffp06NCh8+bNY3ZGR0e3adPG2Nj4S4eteXB3FABAPdna2vq+4+zszDTkWDk4ODx79oxUNY7QzMxs/fr1lXZu27btC8aq0bzE1J0sJEIAAA3Wr1+/iIiIn376ib1FCB+GNSgAADRdcHBw586dPTw8kAhrArdGAQA0HUVR69evv3fvHhJhTVgaECGPpBarOg4AAPg8VlZWSIQ1hPllAAC0w/8nwuTk5NjYWBWGollwdxQAQDv8fyI8f/78pEmTmG03N7dr166pKCTN4ClGvQwAgDb4/0QoEolyc3NpmiaE5OXlSaVS1UWlAdAiBADQDv8/jtDX1zczM7NTp04NGzYsLCxcv3790aNH33/Bzz//zGF46guJEABAO/x/IrS3t//tt98WL168Y8eOkpKSI0eOCAQsw+2RCBkW+kSfT94U0zZGmGgNAECD/SvV9ezZs2fPnoQQa2vrQ4cOKaZrA1ZeYio+h9gYqToOAAD4DOzDJzZv3uzm5sZxKBoHd0cBALQA+1yjXbp0IYSkpaU9ePDg1atXdevW9fT0tLOz4zY2decpom5hxlEAAA3H3iKUSqWRkZF2dnbBwcFDhw4NDQ11cHAYOHAgs2bvRyUlJY0ZM8bX17dJkybjx49XLGr/66+/tq8gMzPz/ddevXq1Xbt27u7u3333XWFhYY3/MA6gRQgAoAXYW4Q//fTT+vXrhw4dGh4ebm1tnZmZefTo0fXr1/N4vN27d3/0pAkJCXXr1t24caNQKJw8efLQoUOZAtTnz58bGhqOHz+eOczU1LTSCwsKCjp27Lho0aL27dt///33P/zww8aNGz/vD1QibzEVn0PThKBaBgBAc7EkQolEsmHDhv/85z+zZ89W7Gzbtq2Hh8eYMWOioqIsLCw+fNIuXbowN1cJIbNnzw4LC1M8Va9eveDg4KpeuG/fvgYNGowcOZIQsmTJEl9f32XLlr2fL9WEWJ8Y8smbItrWGKkQAEBTsdwazcjIyM/P79WrV6X9vXv3lslkz58//6QLXL9+3cvLS/EwNja2devW/fv3v3Tp0vsHx8fHN2vWjNl2d3fn8XjMqolqy0tMxeeqOggAAPgMLC1CMzMzHo+XlJTUsGHDivuTkpIIISKRqPpnf/DgwZw5c44fP848bNWqlZeXl7W19d9//x0SEnLy5Mk2bdpUPD4jI8PR0VHxUCQSpaenV3Xy5OTkigffvHlTKBRWP7YvwsVYcOutpKWZsmbhoWm6pKRELpcr6fzaSs17l9VTaWmpQCBgHT0MH4APWw1w9qaVlJQw06V9GMuH3sTEpG3btpGRkRYWFq1bt2Z23r17NyIiwtPT08XFpZoRPHnyJDQ0dO3atYrxiEFBQcxGy5YtU1JStm3bVikRikSiivU4+fn5YrG4qvPb2toeOnRI8VAsFlMU17cofazkNzJoU1NDJZ2fpmk+n29sbKyk82sxtb2jrraEQiESYc3gw1YD3LxphoaG1ckL7B/6DRs2tG3bNiAgwNra2sbGJiMj49WrV2Kx+OTJk9W8fFJSUvv27efPnz9gwADWA2rXrp2amlppp6Oj49mzZ5nt1NTUoqIiBweHqi4hFAqdnJyqGY+SeImonU/QXAMA0GDswydcXFwePHiwYsUKf39/c3Pzxo0bz58//+HDh82bN6/OSVNSUoKCgr777rvu3bvn5OTk5OQwjdO4uDiJREIIefjw4ebNm9u3b88cP2PGjKdPnxJCmL7DW7duEUJWrFgRFhZWu3btL/J3KomXmErIrUbDGwAA1FWVt0HMzc0nTpw4ceLEGpz05MmTBQUFS5YsWbJkCbPnxYsXJiYmK1asCA0NNTQ0JISMHTt2xIgRzLN79+4NCwtzdXW1tbVds2ZN+/bt+Xx+vXr1oqOja3B1Lon1iZGAvC6i7VA4CgCgmZTSHzBq1KhRo0a9v//IkSNSqbS4uNjMzKzi/uTkZMX2t99+O3jw4KKiokrHqC1PERWfQ+zYevEKCwt3bNz45NYtp4YNh44e/Ul1RgAAwA2uO8YFAsFHMxyfz9eULEje3R0NtavcIkxLS+vYvHlEWlr/8vIHQmHbNWuOXLlib2+vkiABAKAq7H2EUH1VTbQ2a9y4Ba9fjy0vb0nICIlkbWrqtOHDuQ8PAAA+DInwc1WVCO/euBFcYfxfK5p++vAhh3EBAEC1IBF+Li8xlZDDUjhqamqaX+FhKSEGhsoabggAADWGRPi5RHrEREi9KqqcCvuNHDnTzExGCCGEJmSWiUnPwYO5Dw8AAD6MvVhGLpcfOXIkJibm7du3leb3On36NCeBaRJPMUnIIfX+XTg6bOzYNykpzXbscOfxHstkoX36fD9jhooCBACAKrEnwsmTJ69cubJ+/frOzs7cz1umcZhuwvcLR/+zZMnU+fNfvnxpZ2enr6+vktgAAODDWBJheXn52rVrp02btnDhQmTB6vASUf+ks08vo6en5+zszHE8AABQfSx9hPn5+eXl5X369EEWrCYvMRWfi3nWAAA0EksitLS09PT0vHfvHvfRaKiqCkcBAED9sfcRbtu2bdCgQcbGxu3bt8fEYB9lrkdMhdSrIroeZhwFANA07ImwQYMGXl5effv2ff+p6ixyqIO8xCT+vcJRAABQf+yJsG/fvpcuXfrmm29cXV1R7lgdTOFo2HuFowAAoOZYEmF2dvaZM2e2bdv27bffch+QhvISU5fT0FYGANA8LMUyfD6foqhGjRpxH43m8hRRCSgcBQDQQCyJ0NzcPDg4+MSJE9xHo7lQOAoAoKGqnFlmxIgRmZmZoaGhVlZWFZ/y9fXlJDANY65HzPSol4W0vQm6CQEANAl7Ihw8eHBaWtrq1atXr15d6SlUjVbFS0Tic4i9iarjAACAT8GeCA8cOFBeXs5xKJqOmV+mQz20CAEANAl7IgwICOA4Di3gicJRAACNsmrVqrlz52I9wi+mqqXqAQBADd28eXPOnDmXL19mbxGGhIRkZ2ezPnXjxg1lBqbBvMTUw1yaJgT3RgEA1N+OHTv69u3r5ubGnggdHBwqTjGal5d369at8vLy0NBQriLUPGZCYq5HvSikHVA4CgCgIiUlJaWlpcx2YWHhBwo8nz592qlTJ1JVH+HmzZsr7SkoKOjbt2+DBg2+UKjaiZlx1AGFowAAKrJkyZJVq1Yx2xKJpEWLFlUdmZOTY2JiQlgH1LMyNTVdvHjxkiVLqrplCgTdhAAAqjZr1qzsd3bv3m1mZlbVkZaWlnl5eaT6iZB5jUQiSU5O/vxAtRUmWgMA0BQeHh53794l1U+EEolkxYoVFEU5OTkpMzDNhhYhAICmiIiIOHToUFxcXLWqRqVS6YsXL3JycsaOHYt1ej/AE4WjAAAawtPTc+PGjZGRkdWqGuXz+cHBwWFhYcHBwVxFqJHMhESsR6UU0PVNkQoBANRdeHh4eHh4datGoZq8xCQ+l9Q3VXUcAABQPdXqI5TL5W/evMF029XhKaYS0E0IAKA52BPh3LlzN2zYwGw/evTIycnJ1ta2Xr16N2/e5DA2jYR6GQAAzcKSCOVy+bJly+rUqcM8/OGHHyQSybp161xdXYcNG4Z24Yd5iZAIAQA0CUsfYU5OTkFBgaenJyGkoKDgzJkzUVFR3333Xfv27V1dXd+8eWNra8t5nBqDmXFUThMeymUAADQBS4tQJpMRQgQCASHk3LlziilGHRwcCCGpqancRqhhTITEwoBKKUSjEABAM7Akwtq1a5uZmZ08eZIQsmvXLhcXF2YQ/evXrwkhGEf4UZ4ikpCr6iAAAKB6WBIhRVHff//9hAkT6tevf+jQobFjxzL74+LiTExMmHYhfADqZQAANAj7OMI5c+a4u7vfuHGjWbNm/fv3Z3ampqZOmjRJKBRyGJ5G8hJTF1KRCAEANAN7IqQoasCAAQMGDKi4c9q0aZyEpPG8xNTaBLmqowAAgGr5hNUnoJo8RdSjXFqONiEAgCZAIvzyUDgKAKBBlJII8/Ly+vXrZ2lpaWBg0KhRo9OnTzP7Dx065Ovra2RkJBKJhg4dWlhY+P5rfX19nd8ZN26cMsLjALNUPQAAqD+lJMLS0lJ/f/8HDx4UFxdHRkb27NkzNzeXEFJeXr5s2bKcnJzHjx8/fvz4v//97/uvTUlJ2bx58+nTp0+fPj1z5kxlhMcBzC8DAKAplJIIraysJk6caG1tzePxhg8fLpFIEhMTCSHh4eFt27bV19e3srIKDw9nlgZ+X7169ZycnJycnKysrJQRHgcwggIAQFMovY/wzJkzJiYmHh4eFXfSNH38+PGvvvqK9SVBQUH16tXr1atXUlLSB84sl8tzKviSQX82TzGVkItECACgAdiHT5SWlq5fv/7w4cOpqaly+b9GAnw4OVXy8uXLiIiINWvWGBsbV9y/ePHily9fHj58+P2X7N6928fHp6ysbP78+R06dLh3756BgQHryZ8/f85MeaMITE9Pr/qxKZWDkHqYI8wvKPzMGUdpmi4pKcFE55+qqKiIojDZ66cpLS0VCATM3IpQffiw1QBnb1ppaWl1vj/ZP/RDhw7dv39/QEBAYGAgj1fDVmNqamq7du1++OEHxZB8xtq1azdt2hQXF1cpOzI6dOjAbKxfv14sFt+7d69Fixas53d2dr5+/XrNYlM2E0JqG0ozibGTyWf9Y9M0zePxWN8o+ACapk1MTFQdhYYRvKPqQDQMPmw1wNmbZmBgUJ2My/KhLy8vP3z48MKFC6dPn17jy6enp7dv337o0KETJ06suH/btm1Lliw5f/58vXr1PnwGmqZpmtbcn1peYhKfQzuZamr8AAA6gqW1l5ubW15eHhYWVuOT5uXltWvXzt3dPTQ09ObNmzdv3szLyyOE7N27d9y4cUuWLMnJybl582Z8fDxz/Jw5c/bs2UMISUhIiImJefPmTVJS0siRI21tbRs1alTjMFTLS0xhBAUAgPpjaRHWrl3b2dk5ISHBx8enZifNzMy0trbOy8tTzMo2b948f3//lJSUVq1abdmyhdlpb2+/detWQkhRUVFJSQkhRCaTRUVFJSYmGhgY+Pv7nzx5Ul9fv2YxqJyniDr3Bn17AADqjiURUhS1devWUaNG2dratmnTpgY3J52dnRWD6CuaPn066+3WJUuWMBsNGza8cOHCp15OPXmJqTWqm3FUKpVu37Dh/OHDJmZmvUeNCvmM9j0AgHZj7xifMmXK27dv27Zta2hoWLdu3YpPfVLVqC7zFFOPcmkZTfic9xLSNN2jTZsW9+7NKSwsIOTnCxfuTZgwedYsruMAANAE7IkwICCgSZMmHIeiZYwFpI4hlVxAO5txnQljY2Prx8f/590Mdr/l5vr98svoH35AbRsAwPvYE+HSpUs5jkMreYlIfI4KEuGdy5cD8/IUD/mE+BGSkJBQ1UAUAABdhtUnlMhLTMXnquC6Ns7Or/5dZPSKz7exsVFBKAAAaq/KwbMlJSUXL1589uxZfn5+xf1Tp05VflRawlNMnXmtgsLRjp06hdWq1TE11ZUQQsgRHk/i4GBnZ8d9JAAA6o89Ed6/f79Dhw6vX79+/ykkwurzElOrHqigcNTS0nLLiROjBw0qy8gop2m3Zs127dzJfRgAABqBPRGOGjXKwsLi9OnTixcvtrOzGz9+/B9//DFnzpzNmzdzHJ9G8xRRj/NUUzjapEmTs/fvl5WVCQQCPp/P9eUBADQHSyKUSqU3btw4cuSIh4cHRVEymaxOnToRERHm5uaDBw9+9eoVZiOsJiMBsTKknhfQLpzXyzA0dzoCAADOsBTLZGZmSiQSZ2dnQoiJiUneu/rD0NDQtLS0R48ecRqghmNmHFV1FAAAUCWWRGhpaSkQCNLT0wkh9erVU6zwkJKSQgjBfbZP4imiElRROAoAANXEkggFAkGLFi2Yqc769Olz586d/v37L1u2rHfv3g4ODi4uLlzHqMmwVD0AgJpjH0e4dOlSZtkHR0fHDRs2/P3331OmTNHT0ztw4IBQKOQ2Qs2GRAgAoObYy15atWql2B42bNiwYcMkEglSYA14iKineXReOTHXU3UoAADA5iMzy0ilUqZYBlmwZowEZJgb75sLUjmahQAAaqnKRLhz586GDRsaGRl5eXkxe2bPnj1v3jyuAtMey/34hRIy+5ZM1YEAAAAL9kS4bt26b7/91t3dffTo0Yqd7u7uq1atkkqlXMWmJQQ8Eh0s+DWRPvhcZcsTAgBAVVgSoVwunzNnzvTp0w8ePNirVy/Ffj8/v6ysLNZ51+DDLPTJ78H8yMsyFM4AAKiV27dvsyTCtLS09PT08PDwSvvr1KlDCGHGF8KnamJBLffj9zwjyy1XdSgAAECITCbr3r17eHg4SyI0NDQkhFRadIIQkpiYSAixsLDgID6t9I0LL9SOCj8nlaFZCACgasePH4+Pj2dvEYpEoiZNmqxcuVImk1HU/ybJlEqlc+fOdXJycnR05DZUrbLCj18mI3NROAMAoGq///577969jYyM2McRLlu2LCwszN/f39vbu7i4eN68eYcOHbp79250dLQiNUINMIUzzWOkDWvJeztiVWQAgC/s/v37ijmxr1+/LpFIqjryxYsXLVu2JFUNqG/Xrt3Zs2enTp26c+dOmqb/+9//enp6xsTEdO3aVRlx6xSmcCbspNRDRHmJtfNXxbVr13atXJmXnd0yNHR4ZKSeHmYTAACO3Lt378iRI8z269evTUxMqjqyrKyMGSJf5YJKAQEBV65cyc3NzcrKMjU1ZSpl4IvwsaCW+fF7npFd7SYQaV2O2L1ly56pU/+bnW1ByLG//grbufPUzZtYugsAuDFw4MCBAwcy2zExMTurXpbc2to6KyuLfHRmGZFI5OzsjCz4xQ1y4YXYUoMvyLRsxhmappfNnHk4O/srQtwJ+bG4uE1i4m979qg6LgCAyvz8/OLi4sgHWoTJycl//PFHamqqTPavyo6ff/5Z6dHphpX+/OA/pXNvy2Y31Z6VrVJSUhoQYlRhT1Bx8eELFwYNHaqqkAAAWEVERCxdunTWrFnsiXDHjh0jR46USCSmpqaVbmohEX4pAh7ZHyRocUTqLdaewpk6deq8pf/VyH1NUTbOzqqKBwCgKrVr1758+fIvv/zCPrPM+PHjO3XqlJaWlp+fn/1v3MeqxawMSUx7/lgtmnHGyMjIvmnTne9+PL0hZGXt2r2++Ua1UQEAsGrQoMHq1atZEmFmZmZBQcGMGTPQNcgBReFMnrbMOLN+//6/unVrVqdOmzp1+ri4LN2/H2NPAUCdsdwatbS0tLa2TktL4z4a3TTIhXc1nR50QRbTns/T/PEUZmZmm6OjZTJZWVmZkZHRx18AAKBSLC1CHo+3dOnSGTNmJCUlcR+Qblrpz8+X0HNva8+MM3w+H1kQADQCe7FM//79z5496+7u7uTkZGpqWvGpGzducBKYbhHyyP4gQfMYaUOxvJe2FM4AAGgE9kQ4bty4HTt2uLi4uLq64nc9N6wMyZEQfthJqbv2zjgDAKCGWBJhUVHRpk2bfvrpp/nz53MfkC5TFM5c6yYw17oZZwAA1BPLXbji4mKZTNazZ0/uo4FBLrxgW2pwnLbNOAMAoLZYEmHt2rU9PT3v3r3LfTRACIny5+eV0/Nuy1UdCACATmDvI9y1a9fgwYP19fVDQkIsLS05jknHKQpnvMWkZ310Fn6yJ0+e3L5928PDo1GjRqqOBQA0AHsiZKaVUUzgXRFN456d0jEzznSIlbqL+PXRWVhtMplsaNeu+dev+xYXHzE0zHVyOnjunLGxsarjAgC1xp4IZ86cWVRUxHEoUFFTS2qZH7/XGfn59hS+yKspasECj7i4GcxHt6hoT37+T2PGRFW9CAsAAKkqEUZGRnIcB7xvkAvvn3R6xD+Co6FEC2ac4cDJ6Oj9FX7ADSwvX3PhgurCAQDNgLHbam2lHy+vnJ5/B4Uz1SKTySr+sqMIkcvx1gHARygrEcrl8pcvXyYnJ1dazpAQkpKSkpyc/IHXZmdnP3z4UCKRKCk2DSLkkV1fybY8kh9Kxhf6xwV27PirgYHi4XGBoHHz5iqMBwA0QpUL836O8+fPDxo0iBCir69P0/TevXv9/f0JISUlJd27d3/8+DEhxM3NLSYmxtDQsNJrf/755yVLltjb2+fk5Bw7dgyFf3UM6HeFM5SnCHdIP2Tq/Pk9Ll68nZjYIicn3tz8mpXVsS1bVB0UAKg7pbQILSwsTpw48erVq6SkpEGDBg0fPpzZv3HjxsLCwqdPnyYmJhYXF2/YsKHSC5OSkhYsWHD9+vU7tGfLMgAAIABJREFUd+6MHj16woQJyghP4zS1pJa24Pc8rT1LNSmJvr7+iatXvzl8uGj58g7791+Mj69Vq5aqgwIAdaeURNioUSNFSy44OPjly5fM9r59+4YNGyYUCgUCwbBhw/bt21fphfv372/Xrp2zszMhZOTIkXFxcampqcqIUOMMduUF2WDGmWoJCAiIGD48JCSEz+erOhYA0ABKL5bZuXNn586dme3k5GQmyRFCnJ2dU1JSKh2ckpLi4uLCbFtYWJibm7948aKqM5eVld2sQOsHOK5qyc8toxegcAYA4ItSSh+hwubNm0+fPn3t2jXmYVFRkcG7WgYjI6P8/PxKxxcVFVWcyMbIyKigoKCqk6elpY0YMULx8OzZs0Kh8IuFrh5omi4pKVHk+O3+VOBpPQ/jsjAb7Vm5UBmKioooCv2pn6a0tFQgEAgEyv1O0D74sNUAZ29aaWlpddpISvzQ79mzZ86cOefPn7eysmL2WFlZ5eTkMNtZWVnW1taVXlLxAEJITk7O+8co2NvbX79+/UtHrV5omubxeIq5UUxMyMF2dOdTJK6zAQpnPoCmaRMTE+4veujgwTMHDxqZmvaMiPjq6685DuAzCd5RdSAaRiUfNk3H2ZtmYGBQnYyrrFuj0dHRU6ZMiY2NdXV1Vez08fH5559/mO1//vmnadOmlV7VpEkTxQF3794VCoVOTk5KilBD+dWhlvuhcEYdDena9dLIkRHR0V23b1/SrduqhQtVHREAVItSEuG5c+f69+/fu3fvv//+e9OmTZs2bWIGBUZGRq5evfrIkSNHjx5dtWqVYv6axo0bX758mRDSu3fvtLS0OXPmXL58edy4ccOHD8eywO8b7Mpra0MNiZNpeaeoRvnrr7+oy5ej8vKaE9KGkN+zs/esXJmXl6fquADg45TVIoyIiKhYzMIMq2/Tps22bds2bdq0cePGrVu3BgYGMge3bt1aLBYTQgwNDc+dO5eYmDhz5sz27dsvxG/qKqxuyc8poxdgqSa1cePvv0Mq3NUXEPK1XH7//n0VhgQA1aSU/oCgoKCgoCDWp7p37969e/dKO3/55RfFtpub2+7du5URlTYR8siBdoIWR6RNLKjO9ugsVL06dnZv9fVJaaliT5pQWKdOHRWGBADVhLlGNZWVIdkfxP/2ovRhLm6Rql5IaOhekUgx1ucSIa8tLBRjgQBAnaFCTIP516GW+fF7nJZd7SYwx7KFKmVpabnu8OEBgwcb5+eXEWLq6LjnwAEeDz80ATQAEqFmG+LKu5JGD4mTHW7Pxx1S1Wrh7//XkyfZ2dn6+vpYDRhAg+AXq8Zb04qfjcIZtVGrVi2VZEG5XH7r1q3Tp0+np6dzf3UAjYZEqPGYwplNj+R/vEBnoY5KTk5u7eGxLjT0fJ8+3b29V8yZo+qIADQJbo1qA2tDcqAdv8sp6cXOAg/MOKN7RvboserJk2aEEELkhPRavbp527atAwJUHBaAhkCLUEswhTM9z8jysZ6xjiksLJSlpjZ795BHyJjs7D/fW9oFAKqCRKg9hrjyAqypwRcw44xuKSsrq3Rjx4CQ0uJi1UQDoIGQCLXKL6342WX0QizVpEssLCwKTU2TK+z51dw8sEcPVcUDoHGQCLUKUziz8aH8+Es0C3XImn37etWtu9jAYAdF9bWwoMLCunbrpuqgADQGimW0DQpndFBTX9/zjx6dOHEiMzV1SuvWzZo1+/hrvqgXL14kJCQ4ODh4eHhwfGmAz4dEqIX861Dzm/F7npFd7SYw07a1ioGdmZlZeHg499elaXpM//5J5875lZdv09Mrdnbed/o01ucDzYJbo9pplDsvwJoagsIZULKNUVHiEydOZWTMy8s7kJEx4NataaNGqToogE+DRKi1VrfkZ5TSgX9I9yXJy2Sqjga01PHffhtfUKB4OKC8/NrFiyqMB6AGkAi1lj6fxHUW/MeHfyiZtt0rGfWX7H422ofwhZWXl1ea752Wo2gZNEZmZuaKFSuQCLUZnyLBttSBdvw7PQVOplTnU7JmMdJNj+TFUlVHBtqiTceOew0NFQ8vUJSrt7cK4wGovoKCghYtWty5cwfFMjrBzpia2pia3Ih3/g296ZF8xnVJL0feGE9e41ooK4XP8sOsWd3PnElITPTPyXliYnK6Tp0jO3dyGYBEInn8+LG+vr6TkxOfz+fy0qDp9uzZY2tru2vXLiRCHcI0EINt+a+LeHsS6W6nZJYGZKQ7b6ALzxgfBKgRfX39E1evnj9//sHt281dXGZ17CgUclepfDY2dsqwYY3Ky8so6pmJyaZDhxo1bszZ1UHTnTt3LiwsjGD4hG6yNaamNqZ+bMQ7V6GB+J0Hr4kFGojwySiKCgoKCgoK4vi6aWlp04YMOZWWZkEIISQxPb1/165/P32qp4dVqnXaoUOHYmNjme3k5GSKqvJrLTU1NSQkhCAR6jLeuwbim2Le7qd0jzMyC30y0p03wJlngtGHoPbOnzvXLz/f4t1DF0J8S0ru37/v6+uryrBA1WxtbRWfAT6fn5KSUtWRfD5fLpcTJEIghNgY/auBOP26pLcjb7QHzwcNRFBjBXl5ZpJ/LbZiJpXm5+erKh5QE35+fn5+fsx2TEzMzqo7rW1sbFJTUwmGT4AC712J6YNeQidTqteZ/5WYFmJdJ1BLLb/+OkYkUgwJKiPkvEDg4+OjyphAo3Tq1OnIkSMymQyJECqra0SmNuYl9hX83Jx/5jXtsE8y6i/ZzUyMQQT14u3t3XzgwF4WFocI2UdImKXlmNmzRSIRN1enaXrrunVfN2jQ1sOjf/v2T58+5ea68AX16dNHKBQGBwfj1iiwU/Qgvi3h73wi73tWZsAng115I915Yn1VBwdACCFkTlTUtQEDzh49KjQw2NCnj5ubG2eXXjJr1vOoqJMFBSaEXH/9OrxNmz9u3qxbty5nAcDn09fXv3Tp0p9//olECB9hbUimNuYpehDr75OE2vFGuvOCbdGDCKrXokWLFi1acHxRmqb3bdp0o6CAGbfYnJDJb99u/+WXGQsWcBwJfCZ9ff3u3bs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+      ],
+      "text/plain": [
+       "Plot{Plots.GRBackend() n=2}"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1198,7 +1464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1244,14 +1510,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mEvaluating over 8 folds: 100%[=========================] Time: 0:00:12\u001b[39m\n"
+      "\r",
+      "Evaluating over 8 folds:  25%[======>                  ]  ETA: 0:00:06\u001b[K\r",
+      "Evaluating over 8 folds:  38%[=========>               ]  ETA: 0:00:05\u001b[K\r",
+      "Evaluating over 8 folds:  50%[============>            ]  ETA: 0:00:04\u001b[K\r",
+      "Evaluating over 8 folds:  62%[===============>         ]  ETA: 0:00:03\u001b[K\r",
+      "Evaluating over 8 folds:  75%[==================>      ]  ETA: 0:00:02\u001b[K\r",
+      "Evaluating over 8 folds:  88%[=====================>   ]  ETA: 0:00:01\u001b[K\r",
+      "Evaluating over 8 folds: 100%[=========================] Time: 0:00:12\u001b[K\n"
      ]
     },
     {
@@ -1262,16 +1535,16 @@
        "  per_observation, fitted_params_per_fold,\n",
        "  report_per_fold, train_test_pairs\n",
        "Extract:\n",
-       "┌───────────────┬─────────────┬───────────┬─────────────────────────────────────\n",
-       "│\u001b[22m measure       \u001b[0m│\u001b[22m measurement \u001b[0m│\u001b[22m operation \u001b[0m│\u001b[22m per_fold                          \u001b[0m ⋯\n",
-       "├───────────────┼─────────────┼───────────┼─────────────────────────────────────\n",
-       "│ LPLoss(p = 1) │ 2.64        │ predict   │ [2.38, 2.8, 2.96, 2.89, 1.81, 2.43 ⋯\n",
-       "│ LPLoss(p = 2) │ 17.8        │ predict   │ [19.8, 24.4, 26.6, 14.4, 6.1, 10.6 ⋯\n",
-       "└───────────────┴─────────────┴───────────┴─────────────────────────────────────\n",
-       "\u001b[36m                                                                1 column omitted\u001b[0m\n"
+       "┌───────────────┬─────────────┬───────────┬─────────────────────────────────────────────────\n",
+       "│\u001b[22m measure       \u001b[0m│\u001b[22m measurement \u001b[0m│\u001b[22m operation \u001b[0m│\u001b[22m per_fold                                      \u001b[0m ⋯\n",
+       "├───────────────┼─────────────┼───────────┼─────────────────────────────────────────────────\n",
+       "│ LPLoss(p = 1) │ 2.64        │ predict   │ [2.38, 2.8, 2.96, 2.89, 1.81, 2.43, 2.52, 3.29 ⋯\n",
+       "│ LPLoss(p = 2) │ 17.8        │ predict   │ [19.8, 24.4, 26.6, 14.4, 6.1, 10.6, 16.9, 23.7 ⋯\n",
+       "└───────────────┴─────────────┴───────────┴─────────────────────────────────────────────────\n",
+       "\u001b[36m                                                                            1 column omitted\u001b[0m\n"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1284,7 +1557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1303,7 +1576,7 @@
        "2.64 ± 0.17"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1340,90 +1613,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Training Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: final loss: 18.029846309038444\n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/train.jl:44\n",
-      "┌ Info: final training loss: 0.15870439154333713\n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/train.jl:46\n",
-      "┌ Info: Stop triggered by NumberSinceBest(6) stopping criterion. \n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/stopping_controls.jl:54\n",
-      "┌ Info: Total of 13 iterations. \n",
-      "└ @ MLJIteration /Users/anthony/.julia/packages/MLJIteration/jtWxG/src/core.jl:35\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "import EvoTrees ✔\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: For silent loading, specify `verbosity=0`. \n",
-      "└ @ Main /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/loading.jl:168\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "import MLJLinearModels ✔"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: For silent loading, specify `verbosity=0`. \n",
-      "└ @ Main /Users/anthony/.julia/packages/MLJModels/Ci1zC/src/loading.jl:168\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Training Machine{EvoTreeRegressor{Float64,…},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Training Machine{LinearRegressor,…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: Training Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…}.\n",
-      "└ @ MLJBase /Users/anthony/.julia/packages/MLJBase/GxHVg/src/machines.jl:464\n",
-      "┌ Info: final loss: 18.029846309038444\n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/train.jl:44\n",
-      "┌ Info: final training loss: 0.15870439154333713\n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/train.jl:46\n",
-      "┌ Info: Stop triggered by NumberSinceBest(6) stopping criterion. \n",
-      "└ @ IterationControl /Users/anthony/.julia/packages/IterationControl/lO4bA/src/stopping_controls.jl:54\n",
-      "┌ Info: Total of 13 iterations. \n",
-      "└ @ MLJIteration /Users/anthony/.julia/packages/MLJIteration/jtWxG/src/core.jl:35\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "[ Info: Training Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…}.\n",
+      "[ Info: final loss: 18.029846309038444\n",
+      "[ Info: final training loss: 0.15870439154333713\n",
+      "[ Info: Stop triggered by NumberSinceBest(6) stopping criterion. \n",
+      "[ Info: Total of 13 iterations. \n",
+      "[ Info: For silent loading, specify `verbosity=0`. \n",
+      "import EvoTrees ✔\n",
+      "[ Info: For silent loading, specify `verbosity=0`. \n",
+      "import MLJLinearModels ✔\n",
+      "[ Info: Training Machine{EvoTreeRegressor{Float64,…},…}.\n",
+      "[ Info: Training Machine{LinearRegressor,…}.\n",
+      "[ Info: Training Machine{DeterministicIteratedModel{DeterministicPipeline{NamedTuple{,…},…}},…}.\n",
+      "[ Info: final loss: 18.029846309038444\n",
+      "[ Info: final training loss: 0.15870439154333713\n",
+      "[ Info: Stop triggered by NumberSinceBest(6) stopping criterion. \n",
+      "[ Info: Total of 13 iterations. \n",
       "┌────────────────────────────┬────────────────────┐\n",
-      "│\u001b[1m models                     \u001b[0m│\u001b[1m mean_square_errors \u001b[0m│\n",
-      "│\u001b[90m String                     \u001b[0m│\u001b[90m Float64            \u001b[0m│\n",
-      "│\u001b[90m Textual                    \u001b[0m│\u001b[90m Continuous         \u001b[0m│\n",
+      "│ models                     │ mean_square_errors │\n",
+      "│ String                     │ Float64            │\n",
+      "│ Textual                    │ Continuous         │\n",
       "├────────────────────────────┼────────────────────┤\n",
       "│ EvoTreeRegressor           │ 3.59326            │\n",
       "│ LinearRegressor            │ 3.56423            │\n",
@@ -1460,10 +1676,6 @@
   }
  ],
  "metadata": {
-  "@webio": {
-   "lastCommId": null,
-   "lastKernelId": null
-  },
   "kernelspec": {
    "display_name": "Julia 1.6.5",
    "language": "julia",
diff --git a/examples/boston/notebook.jl b/examples/boston/notebook.jl
index 6dbdf005..da38986a 100644
--- a/examples/boston/notebook.jl
+++ b/examples/boston/notebook.jl
@@ -177,7 +177,6 @@ iterated_pipe =
     IteratedModel(model=pipe,
                   controls=controls,
                   resampling=Holdout(fraction_train=0.8),
-                  iteration_parameter=:(neural_network_regressor.epochs),
                   measure = l2)
 
 # Training the wrapped model on all the train/validation data:
diff --git a/examples/boston/notebook.unexecuted.ipynb b/examples/boston/notebook.unexecuted.ipynb
index 4f9ddc95..4895e2dc 100644
--- a/examples/boston/notebook.unexecuted.ipynb
+++ b/examples/boston/notebook.unexecuted.ipynb
@@ -456,7 +456,6 @@
     "    IteratedModel(model=pipe,\n",
     "                  controls=controls,\n",
     "                  resampling=Holdout(fraction_train=0.8),\n",
-    "                  iteration_parameter=:(neural_network_regressor.epochs),\n",
     "                  measure = l2)"
    ],
    "metadata": {},