fix up missing text

Author: ablaom

notebooks/04_tuning/gamma_sampler.png

@@ -78,6 +78,7 @@ iterator(r, 5)
 #-
 
 using Plots
+gr(size=(490,300))
 _, _, lambdas, losses = learning_curve(mach,
                                        range=r,
                                        resampling=CV(nfolds=6),
@@ -142,9 +143,10 @@ r = range(model,
           unit=5,
           scale=:log10)
 
-# The `scale` in a range makes no in a `RandomSearch` (unless it is a
-# function) but this will effect later plots but it does effect the
-# later plots.
+# The `scale` in a range is ignored in a `RandomSearch`, unless it is a
+# function. (It *is* relevant in a `Grid` search, not demonstrated
+# here.) Note however, the choice of scale *does* effect how later plots
+# will look.
 
 # Let's see what sampling using a Gamma distribution is going to mean
 # for this range:
@@ -189,8 +191,6 @@ predict(tuned_mach, rows=1:3)
 rep = report(tuned_mach);
 rep.best_model
 
-# By default, sampling of a bounded range is uniform. Lets
-
 # In the special case of two-parameters, you can also plot the results:
 
 plt = plot(tuned_mach)

notebooks/04_tuning/learning_curve2.png

@@ -175,7 +175,7 @@ md"Now for a pipeline model:"
 LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels
 
 # ╔═╡ df32389d-809d-4927-9380-d0b65e00252c
- base_model = Pipeline(Standardizer, ContinuousEncoder, LogisticClassifier)
+ base_model = Standardizer |> ContinuousEncoder |> LogisticClassifier
 
 # ╔═╡ a4225c7f-bb3f-479b-a0cd-3f0d573aca2f
  base_mach = machine(base_model, XHorse, yHorse)
@@ -282,9 +282,8 @@ r_lambda_unbound = range(base_model,
 
 # ╔═╡ 8729b9a8-ac7a-485a-b950-fddef2a1fb10
 md"""
-The `scale` in a range makes no **???** in a `RandomSearch` (unless it is a
-function) $(HTML("<del>but this will effect later plots</del>")) but it does effect the
-later plots.
+The `scale` in a range is ignored in a `RandomSearch`, unless it is a
+function. (It *is* relevant in a `Grid` search, not demonstrated here.) Note however, the choice of scale *does* effect how later plots will look.
 """
 
 # ╔═╡ 28aaf005-792c-46a4-951b-64cb2a36c8e3
@@ -351,9 +350,6 @@ rep = report(tuned_mach)
 # ╔═╡ e938da11-be0e-4c4a-ab04-579e5608ae53
 rep.best_model
 
-# ╔═╡ f3738cb7-872f-4618-86cf-35420d9e6995
-md"By default, sampling of a bounded range is uniform. Lets"
-
 # ╔═╡ 5b72bfac-40e2-4f84-a29a-8d0445351914
 md"In the special case of two-parameters, you can also plot the results:"
 
@@ -560,7 +556,6 @@ md"""
 # ╟─22392335-e988-49b9-8f3d-b82f1e7b6f7a
 # ╠═4d54c007-95eb-4897-90a0-060e4b5a56de
 # ╠═e938da11-be0e-4c4a-ab04-579e5608ae53
-# ╟─f3738cb7-872f-4618-86cf-35420d9e6995
 # ╟─5b72bfac-40e2-4f84-a29a-8d0445351914
 # ╠═89dfc4b1-37dc-4f77-be65-28d0fcca50a8
 # ╟─efa202a5-5256-473c-9a2e-c468345d87d1

notebooks/04_tuning/notebook.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Machine Learning in Julia"
+    "# Machine Learning in Julia (continued)"
    ],
    "metadata": {}
   },
@@ -189,6 +189,7 @@
    "cell_type": "code",
    "source": [
     "using Plots\n",
+    "gr(size=(490,300))\n",
     "_, _, lambdas, losses = learning_curve(mach,\n",
     "                                       range=r,\n",
     "                                       resampling=CV(nfolds=6),\n",
@@ -310,9 +311,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "The `scale` in a range makes no in a `RandomSearch` (unless it is a\n",
-    "function) but this will effect later plots but it does effect the\n",
-    "later plots."
+    "The `scale` in a range is ignored in a `RandomSearch`, unless it is a\n",
+    "function. (It *is* relevant in a `Grid` search, not demonstrated\n",
+    "here.) Note however, the choice of scale *does* effect how later plots\n",
+    "will look."
    ],
    "metadata": {}
   },
@@ -421,13 +423,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "By default, sampling of a bounded range is uniform. Lets"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [