From 78df387cfde7e15ed1e75cf2b58117f6ad9d05ff Mon Sep 17 00:00:00 2001
From: ablaom <30517088+ablaom@users.noreply.github.com>
Date: Fri, 16 Oct 2026 19:57:48 +0000
Subject: [PATCH] update notebook markdown
---
.../notebooks/UsingMLJ/01_basics/notebook.md | 80 +--
.../UsingMLJ/02_model_composition/notebook.md | 8 +-
.../03_model_tuning/learning_curve.svg | 64 +--
.../UsingMLJ/03_model_tuning/notebook.md | 30 +-
.../03_model_tuning/tuned_model_grid.svg | 534 +++++++++---------
.../03_model_tuning/tuned_model_random.svg | 492 ++++++++--------
learning_curve.svg | 64 +--
tuned_model_grid.svg | 534 +++++++++---------
tuned_model_random.svg | 492 ++++++++--------
9 files changed, 1149 insertions(+), 1149 deletions(-)
diff --git a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
index c3c00d2..389d071 100644
--- a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
@@ -6,8 +6,8 @@ EditURL = "notebook.jl"
Notebook supporting the video series "Using MLJ".
-[Video](https://www.youtube.com/watch?v=6ILPGhuse4o&list=PLHwT4lR4GIv8&index=1).
-[Slides](slides.pdf) from the video.
+- [Video](https://www.youtube.com/watch?v=6ILPGhuse4o&list=PLHwT4lR4GIv8&index=1)
+- [Slides](slides.pdf) from the video
To run the code in this tutorial in a live Julia session, first follow the instructions
given [here](@ref instructions).
@@ -199,8 +199,8 @@ fitted_params(mach)
````
(forest = Ensemble of Decision Trees
Trees: 100
-Avg Leaves: 146.95
-Avg Depth: 14.69,)
+Avg Leaves: 146.16
+Avg Depth: 14.34,)
````
````@julia
@@ -219,9 +219,9 @@ predict(mach, X)[1:3]
````
3-element Vector{Float64}:
- 26.984999999999996
- 22.76
- 34.504
+ 26.796
+ 22.617000000000004
+ 35.983
````
Predict in the `test` rows:
@@ -237,7 +237,7 @@ mae(ypred, y[test])
````
````
-4.639163366336634
+4.776490099009901
````
`mae` is actually just an alias:
@@ -284,14 +284,14 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-978
+Tag: RandomForestRegressor-769
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
-│ LPLoss( │ predict │ 4.64 │
+│ LPLoss( │ predict │ 4.78 │
│ p = 1) │ │ │
-│ RSquared() │ predict │ 0.304 │
+│ RSquared() │ predict │ 0.251 │
└────────────┴───────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -313,21 +313,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-113
+Tag: RandomForestRegressor-276
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
-│ A │ LPLoss( │ predict │ 3.01 │ 0.85 │
+│ A │ LPLoss( │ predict │ 3.07 │ 0.91 │
│ │ p = 1) │ │ │ │
-│ B │ RSquared() │ predict │ 0.65 │ 0.17 │
+│ B │ RSquared() │ predict │ 0.64 │ 0.17 │
└───┴────────────┴───────────┴─────────────┴─────────┘
-┌───┬─────────────────────────────────────────┐
-│ │ per_fold │
-├───┼─────────────────────────────────────────┤
-│ A │ [2.35, 2.29, 3.02, 2.58, 4.9, 2.92] │
-│ B │ [0.695, 0.844, 0.763, 0.8, 0.443, 0.38] │
-└───┴─────────────────────────────────────────┘
+┌───┬───────────────────────────────────────────┐
+│ │ per_fold │
+├───┼───────────────────────────────────────────┤
+│ A │ [2.16, 2.37, 3.36, 2.54, 5.0, 3.0] │
+│ B │ [0.729, 0.817, 0.72, 0.806, 0.393, 0.393] │
+└───┴───────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -350,21 +350,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-528
+Tag: RandomForestRegressor-837
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
-│ A │ LPLoss( │ predict │ 2.48 │ 0.12 │
+│ A │ LPLoss( │ predict │ 2.5 │ 0.18 │
│ │ p = 1) │ │ │ │
-│ B │ RSquared() │ predict │ 0.826 │ 0.021 │
+│ B │ RSquared() │ predict │ 0.814 │ 0.046 │
└───┴────────────┴───────────┴─────────────┴─────────┘
-┌───┬───────────────────────────────────────────────────────────────────────┐
-│ │ per_fold │
-├───┼───────────────────────────────────────────────────────────────────────┤
-│ A │ [2.65, 2.39, 2.37, 2.92, 2.48, 2.45, 2.41, 2.32, 2.51, 2.3] │
-│ B │ [0.816, 0.809, 0.808, 0.791, 0.839, 0.822, 0.86, 0.843, 0.781, 0.886] │
-└───┴───────────────────────────────────────────────────────────────────────┘
+┌───┬──────────────────────────────────────────────────────────────────────┐
+│ │ per_fold │
+├───┼──────────────────────────────────────────────────────────────────────┤
+│ A │ [2.33, 2.55, 3.04, 2.13, 2.72, 2.12, 2.38, 2.63, 2.68, 2.47] │
+│ B │ [0.855, 0.823, 0.69, 0.885, 0.768, 0.884, 0.878, 0.769, 0.855, 0.73] │
+└───┴──────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -374,8 +374,8 @@ e.uncertainty_radius_95
````
2-element Vector{Float64}:
- 0.12150183577386904
- 0.02074147391875972
+ 0.18403789695002817
+ 0.04574117004868558
````
# Interlude on scientific types
@@ -620,8 +620,8 @@ first(yprob, 5)
5-element CategoricalDistributions.UnivariateFiniteVector{ScientificTypesBase.Multiclass{2}, InlineStrings.String7, UInt32, Float64}:
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
- UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.89, >50K=>0.11)
- UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.24, >50K=>0.76)
+ UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.97, >50K=>0.03)
+ UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.26, >50K=>0.74)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
````
@@ -632,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
- <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
- >50K ┤■■■■ 0.11
+ <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.97
+ >50K ┤■ 0.03
└ ┘
````
@@ -644,7 +644,7 @@ accuracy(ypoint, y[test])
````
````
-0.7822081179300814
+0.7799047960280493
````
````@julia
@@ -652,7 +652,7 @@ log_loss(yprob, y[test])
````
````
-1.6055684252777864
+1.5392550897855832
````
Evaluate with one command:
@@ -672,13 +672,13 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestClassifier-917
+Tag: RandomForestClassifier-419
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
-│ Accuracy() │ predict_mode │ 0.781 │
-│ LogLoss( │ predict │ 1.58 │
+│ Accuracy() │ predict_mode │ 0.782 │
+│ LogLoss( │ predict │ 1.56 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴──────────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
diff --git a/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md b/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
index 9aabbcb..f4bc1c3 100644
--- a/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
@@ -6,8 +6,8 @@ EditURL = "notebook.jl"
Notebook supporting the video series "Using MLJ".
-[Video](https://www.youtube.com/watch?v=ZqSqRLPyyoU&list=PLHwT4lR4GIv8&index=2).
-[Slides](slides.pdf) from the video.
+- [Video](https://www.youtube.com/watch?v=ZqSqRLPyyoU&list=PLHwT4lR4GIv8&index=2)
+- [Slides](slides.pdf) from the video
To run the code in this tutorial in a live Julia session, first follow the instructions
given [here](@ref instructions).
@@ -160,7 +160,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: DeterministicPipeline-790
+Tag: DeterministicPipeline-953
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -225,7 +225,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: TransformedTargetModelDeterministic-136
+Tag: TransformedTargetModelDeterministic-890
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
diff --git a/docs/src/notebooks/UsingMLJ/03_model_tuning/learning_curve.svg b/docs/src/notebooks/UsingMLJ/03_model_tuning/learning_curve.svg
index c57796b..7df8fcb 100644
--- a/docs/src/notebooks/UsingMLJ/03_model_tuning/learning_curve.svg
+++ b/docs/src/notebooks/UsingMLJ/03_model_tuning/learning_curve.svg
@@ -1,46 +1,46 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md b/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
index dd12ac0..de483f0 100644
--- a/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
@@ -6,8 +6,8 @@ EditURL = "notebook.jl"
Notebook supporting the video series "Using MLJ".
-[Video](https://www.youtube.com/watch?v=F0wEEixej3Q&list=PLHwT4lR4GIv8&index=3).
-[Slides](slides.pdf) from the video.
+- [Video](https://www.youtube.com/watch?v=F0wEEixej3Q&list=PLHwT4lR4GIv8&index=3)
+- [Slides](slides.pdf) from the video
To run the code in this tutorial in a live Julia session, first follow the instructions
given [here](@ref instructions).
@@ -94,7 +94,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: ProbabilisticPipeline-299
+Tag: ProbabilisticPipeline-290
Extract:
┌──────────┬──────────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -161,7 +161,7 @@ savefig("learning_curve.svg");
````
[ Info: Training machine(DeterministicTunedModel(model = DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …), …).
[ Info: Attempting to evaluate 30 models.
-
Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00[K
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+
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Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:27[K
````
@@ -238,7 +238,7 @@ savefig("tuned_model_grid.svg");
````
[ Info: Training machine(DeterministicTunedModel(model = DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …), …).
[ Info: Attempting to evaluate 36 models.
-
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+
Evaluating over 36 metamodels: 0%[> ] ETA: N/A[K
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````
@@ -320,7 +320,7 @@ savefig("tuned_model_random.svg");
````
[ Info: Training machine(DeterministicTunedModel(model = DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …), …).
[ Info: Attempting to evaluate 40 models.
-
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````
@@ -335,10 +335,10 @@ predict(mach, X)[1:5]
````
5-element Vector{Float64}:
1.3710151999999998
- 3.627463819999999
+ 3.627463819999998
1.8085951933333335
- 1.9762690599999997
- 1.3529714200000003
+ 1.9762690599999995
+ 1.3529714200000014
````
Evaluate the "self-tuning" pipeline:
@@ -354,7 +354,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: DeterministicTunedModel-572
+Tag: DeterministicTunedModel-212
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -377,9 +377,9 @@ Comparing with the baseline computed earlier:
````
````
-ebase = PerformanceEvaluation("ProbabilisticPipeline-299", 0.568 ± 0.033)
-e0 = PerformanceEvaluation("DeterministicPipeline-781", 0.184 ± 0.028)
-e1 = PerformanceEvaluation("DeterministicTunedModel-572", 0.175 ± 0.021)
+ebase = PerformanceEvaluation("ProbabilisticPipeline-290", 0.568 ± 0.033)
+e0 = PerformanceEvaluation("DeterministicPipeline-995", 0.184 ± 0.028)
+e1 = PerformanceEvaluation("DeterministicTunedModel-212", 0.175 ± 0.021)
````
@@ -395,8 +395,8 @@ describe.([e0, e1]) |> pretty
│ String │ Measurement{Float64} │
│ Textual │ Continuous │
├─────────────────────────────┼──────────────────────┤
-│ DeterministicPipeline-781 │ 0.184±0.028 │
-│ DeterministicTunedModel-572 │ 0.175±0.021 │
+│ DeterministicPipeline-995 │ 0.184±0.028 │
+│ DeterministicTunedModel-212 │ 0.175±0.021 │
└─────────────────────────────┴──────────────────────┘
````
diff --git a/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_grid.svg b/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_grid.svg
index 09128de..1c610c0 100644
--- a/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_grid.svg
+++ b/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_grid.svg
@@ -1,294 +1,294 @@
diff --git a/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_random.svg b/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_random.svg
index 90cf847..c3479ee 100644
--- a/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_random.svg
+++ b/docs/src/notebooks/UsingMLJ/03_model_tuning/tuned_model_random.svg
@@ -1,269 +1,269 @@
diff --git a/learning_curve.svg b/learning_curve.svg
index 7506735..6ef948d 100644
--- a/learning_curve.svg
+++ b/learning_curve.svg
@@ -1,46 +1,46 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/tuned_model_grid.svg b/tuned_model_grid.svg
index 358713d..7fb28cf 100644
--- a/tuned_model_grid.svg
+++ b/tuned_model_grid.svg
@@ -1,294 +1,294 @@
diff --git a/tuned_model_random.svg b/tuned_model_random.svg
index 45f3076..d786800 100644
--- a/tuned_model_random.svg
+++ b/tuned_model_random.svg
@@ -1,269 +1,269 @@