diff --git a/docs/make.jl b/docs/make.jl
index 93325cc..a30a955 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -42,7 +42,8 @@ do_build_notebooks = is_ci
 if do_build_notebooks
     build()
     getting_started::Pair = "Getting Started" => [
-        "Simple Binary Classification" => "binary-classification.md"
+        "Basic Example" => "basic-example.md",
+        "Advanced Example" => "binary-classification.md"
     ]
     insert!(pages, 2, getting_started)
 end
diff --git a/docs/src/basic-example.jl b/docs/src/basic-example.jl
new file mode 100644
index 0000000..21c39a9
--- /dev/null
+++ b/docs/src/basic-example.jl
@@ -0,0 +1,199 @@
+### A Pluto.jl notebook ###
+# v0.19.27
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 8ca5e9f4-539c-11ee-0b5a-ab77d2a5bfbf
+# ╠═╡ show_logs = false
+begin
+	ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
+
+	PKGDIR = dirname(dirname(@__DIR__))
+    DOCS_DIR = dirname(@__DIR__)
+	using Pkg: Pkg
+	Pkg.activate(DOCS_DIR)
+	Pkg.develop(; path=PKGDIR)
+end
+
+# ╔═╡ 1c1bd75a-9266-4256-bfea-ad60dd1c1d1c
+begin
+	using CategoricalArrays: categorical
+	using CSV: CSV
+	using DataDeps: DataDeps, DataDep, @datadep_str
+	using DataFrames
+	using MLJ
+	using PlutoUI: TableOfContents # hide
+	using StableRNGs: StableRNG
+	using SIRUS: StableRulesClassifier
+end
+
+# ╔═╡ 3071aa12-92df-47b2-a5ce-a54a7110ab6a
+md"""
+# Basic Example
+
+This page shows a basic example for using SIRUS.jl on a dataset via the Machine Learning Julia (MLJ.jl) interface.
+"""
+
+# ╔═╡ 0b4f89c4-ccb0-4cc9-b7bb-3f630ba2398c
+md"""
+To show the algorithm, we'll use Haberman's survival dataset.
+We load it via `DataDeps.jl` so that we can use a checksum for verification and to cache the dataset.
+"""
+
+# ╔═╡ 75550619-b310-4c66-9371-93656f78765c
+# ╠═╡ show_logs = false
+let
+    name = "Haberman"
+    message = "Haberman's Survival Data Set"
+    remote_path = "https://github.com/rikhuijzer/haberman-survival-dataset/releases/download/v1.0.0/haberman.csv"
+    checksum = "a7e9aeb249e11ac17c2b8ea4fdafd5c9392219d27cb819ffaeb8a869eb727a0f"
+    DataDeps.register(DataDep(name, message, remote_path, checksum))
+end;
+
+# ╔═╡ 05aaa007-0fe0-44ef-b815-ecf9e5f474f7
+md"""
+After dataset registration, we can load it into a `DataFrame`:
+"""
+
+# ╔═╡ ac2d7dbc-364f-437f-b66f-8eb288395275
+data = let
+    dir = datadep"Haberman"
+    path = joinpath(dir, "haberman.csv")
+    df = CSV.read(path, DataFrame)
+    df[!, :survival] = categorical(df.survival)
+    df
+end
+
+# ╔═╡ 08a4ca2b-bc65-4c29-9528-f4789272143a
+md"And split it into features (`X`) and outcomes (`y`):"
+
+# ╔═╡ e037d952-e489-41b6-afc9-317a8c17e6c4
+X = select(data, Not(:survival));
+
+# ╔═╡ 2f921f63-5148-4726-9839-c84217f60e0b
+y = data.survival;
+
+# ╔═╡ a1764625-4b7a-42f3-9e61-3d26122d86da
+md"""
+Next, we can load the model that we want to use.
+Since Haberman's outcome column (`survival`) contains 0's and 1's, we use the `StableRulesClassifier`.
+We can make the `StableRulesClassifier` symbol available via MLJ's `@load`:
+```julia
+StableRulesClassifier = @load StableRulesClassifier pkg="SIRUS"
+```
+or directly via
+```julia
+using SIRUS: StableRulesClassifier
+```
+"""
+
+# ╔═╡ ccce5f3e-e396-4765-bf5f-6f79e905aca8
+model = StableRulesClassifier(; rng=StableRNG(1), max_depth=2, max_rules=8);
+
+# ╔═╡ 97c9ea2a-2897-472b-b15e-215f40049cf5
+md"""
+Next, we will show two common use-cases:
+
+1. fit the model to the full dataset and
+2. fit the model to cross-validation folds (to evaluate model performance).
+"""
+
+# ╔═╡ b1281f29-61d7-4c43-960c-e516464ea213
+md"""
+## Fitting to the full dataset
+"""
+
+# ╔═╡ c77e3efb-9170-4675-b053-b99cdb8db853
+# ╠═╡ show_logs = false
+mach = let
+	mach = machine(model, X, y)
+	MLJ.fit!(mach)
+end;
+
+# ╔═╡ d90d86de-943e-4cad-a5bd-fecac2681b98
+md"And inspect the fitted model:"
+
+# ╔═╡ 0c9b2e27-beb0-4f12-b8fd-33dc67f598c1
+mach.fitresult
+
+# ╔═╡ 89b12064-5d46-436c-b697-0a4dc527d586
+md"""
+This shows that the model contains $(length(mach.fitresult.rules)) rules where the first rule, for example, can be interpreted as
+
+_If the number of detected axillary nodes is lower than 8, then take 0.136 and otherwise take 0.02._
+
+This calculation is done for all $(length(mach.fitresult.rules)) rules and the score is summed to get a prediction.
+"""
+
+# ╔═╡ 13ad02dd-c557-4599-8502-f85d20234ed0
+md"""
+The predictions are of the type `UnivariateFinite` from MLJ's `CategoricalDistributions.jl`:
+"""
+
+# ╔═╡ e732756b-7aaa-4fcc-b90f-1b418208c5af
+predictions = predict(mach, X)
+
+# ╔═╡ 58711147-9f89-465a-9e21-ab1d64e03c2d
+md"""
+To get the underlying predictions out of these objects, use `pdf`.
+For example, to get the prediction for the class 0 for the first datapoint, use:
+"""
+
+# ╔═╡ ed969c5c-6f58-4b8c-825b-fcf04da74036
+pdf(predictions[1], 0)
+
+# ╔═╡ 1ca8a8b1-0623-47d7-8900-41056e0b21ee
+md"""
+See <https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict> for more information.
+"""
+
+# ╔═╡ ece3f092-368e-41af-994a-e814f2267f48
+md"""
+## Model Evaluation via Cross-Validation
+
+Let's define our Cross-Validation (CV) strategy with 10 folds.
+Also, we enable shuffling to make it more likely that our model sees cases from both `survival` classes:
+"""
+
+# ╔═╡ dfc6f708-3d26-4102-92c6-33cee32e438c
+resampling = CV(; rng=StableRNG(1), nfolds=10, shuffle=true);
+
+# ╔═╡ d2905680-552d-4a9a-b3f1-7dd27cbf703f
+md"""
+We use the Area Under the Curve (AUC) measure since that measure is appropriate for binary classification tasks.
+More specifically, the measure gives the area under the receiver operating characteristic curve.
+For this measure, a score of 0.5 means that our model is as good (or bad, actually) as random guessing, and a score of 0.0 means predicting all cases wrong and 1.0 means predicting all cases correctly.
+"""
+
+# ╔═╡ b8c6c9e0-679e-41d5-80c0-ffd65e652489
+evaluate(model, X, y; resampling, measure=auc)
+
+# ╔═╡ Cell order:
+# ╠═3071aa12-92df-47b2-a5ce-a54a7110ab6a
+# ╠═8ca5e9f4-539c-11ee-0b5a-ab77d2a5bfbf
+# ╠═1c1bd75a-9266-4256-bfea-ad60dd1c1d1c
+# ╠═0b4f89c4-ccb0-4cc9-b7bb-3f630ba2398c
+# ╠═75550619-b310-4c66-9371-93656f78765c
+# ╠═05aaa007-0fe0-44ef-b815-ecf9e5f474f7
+# ╠═ac2d7dbc-364f-437f-b66f-8eb288395275
+# ╠═08a4ca2b-bc65-4c29-9528-f4789272143a
+# ╠═e037d952-e489-41b6-afc9-317a8c17e6c4
+# ╠═2f921f63-5148-4726-9839-c84217f60e0b
+# ╠═a1764625-4b7a-42f3-9e61-3d26122d86da
+# ╠═ccce5f3e-e396-4765-bf5f-6f79e905aca8
+# ╠═97c9ea2a-2897-472b-b15e-215f40049cf5
+# ╠═b1281f29-61d7-4c43-960c-e516464ea213
+# ╠═c77e3efb-9170-4675-b053-b99cdb8db853
+# ╠═d90d86de-943e-4cad-a5bd-fecac2681b98
+# ╠═0c9b2e27-beb0-4f12-b8fd-33dc67f598c1
+# ╠═89b12064-5d46-436c-b697-0a4dc527d586
+# ╠═13ad02dd-c557-4599-8502-f85d20234ed0
+# ╠═e732756b-7aaa-4fcc-b90f-1b418208c5af
+# ╠═58711147-9f89-465a-9e21-ab1d64e03c2d
+# ╠═ed969c5c-6f58-4b8c-825b-fcf04da74036
+# ╠═1ca8a8b1-0623-47d7-8900-41056e0b21ee
+# ╠═ece3f092-368e-41af-994a-e814f2267f48
+# ╠═dfc6f708-3d26-4102-92c6-33cee32e438c
+# ╠═d2905680-552d-4a9a-b3f1-7dd27cbf703f
+# ╠═b8c6c9e0-679e-41d5-80c0-ffd65e652489
diff --git a/docs/src/binary-classification.jl b/docs/src/binary-classification.jl
index 53540fb..1e6fc4b 100644
--- a/docs/src/binary-classification.jl
+++ b/docs/src/binary-classification.jl
@@ -19,8 +19,6 @@ end
 
 # ╔═╡ f833dab6-31d4-4353-a68b-ef0501d606d4
 begin
-	ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
-
 	using CairoMakie
 	using CategoricalArrays: categorical
 	using CSV: CSV
@@ -42,6 +40,8 @@ TableOfContents()
 
 # ╔═╡ b1c17349-fd80-43f1-bbc2-53fdb539d1c0
 md"""
+# Advanced Example: Binary Classification
+
 This page will provide an overview of the algorithm and describe how it works and how it can be used.
 To do this, let's start by briefly describing random forests.
 """