From a29f3ffe4aba45933874aa71371dae4dec0c2eb4 Mon Sep 17 00:00:00 2001
From: ablaom <30517088+ablaom@users.noreply.github.com>
Date: Thu, 15 Oct 2026 20:16:21 +0000
Subject: [PATCH] update notebook markdown
---
.../notebooks/UsingMLJ/01_basics/notebook.md | 69 +--
.../UsingMLJ/02_model_composition/notebook.md | 5 +-
.../03_model_tuning/learning_curve.svg | 64 +--
.../UsingMLJ/03_model_tuning/notebook.md | 21 +-
.../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, 1139 insertions(+), 1136 deletions(-)
diff --git a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
index d9efcfe..c3c00d2 100644
--- a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
@@ -6,6 +6,7 @@ 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.
To run the code in this tutorial in a live Julia session, first follow the instructions
@@ -198,8 +199,8 @@ fitted_params(mach)
````
(forest = Ensemble of Decision Trees
Trees: 100
-Avg Leaves: 146.19
-Avg Depth: 14.53,)
+Avg Leaves: 146.95
+Avg Depth: 14.69,)
````
````@julia
@@ -218,9 +219,9 @@ predict(mach, X)[1:3]
````
3-element Vector{Float64}:
- 26.528000000000002
- 22.439
- 34.312
+ 26.984999999999996
+ 22.76
+ 34.504
````
Predict in the `test` rows:
@@ -236,7 +237,7 @@ mae(ypred, y[test])
````
````
-4.458702970297029
+4.639163366336634
````
`mae` is actually just an alias:
@@ -283,14 +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-330
+Tag: RandomForestRegressor-978
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
-│ LPLoss( │ predict │ 4.46 │
+│ LPLoss( │ predict │ 4.64 │
│ p = 1) │ │ │
-│ RSquared() │ predict │ 0.356 │
+│ RSquared() │ predict │ 0.304 │
└────────────┴───────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -312,21 +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-275
+Tag: RandomForestRegressor-113
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
-│ A │ LPLoss( │ predict │ 3.03 │ 0.87 │
+│ A │ LPLoss( │ predict │ 3.01 │ 0.85 │
│ │ p = 1) │ │ │ │
-│ B │ RSquared() │ predict │ 0.66 │ 0.15 │
+│ B │ RSquared() │ predict │ 0.65 │ 0.17 │
└───┴────────────┴───────────┴─────────────┴─────────┘
-┌───┬────────────────────────────────────────────┐
-│ │ per_fold │
-├───┼────────────────────────────────────────────┤
-│ A │ [2.23, 2.43, 3.37, 2.47, 4.89, 2.83] │
-│ B │ [0.726, 0.811, 0.728, 0.827, 0.438, 0.447] │
-└───┴────────────────────────────────────────────┘
+┌───┬─────────────────────────────────────────┐
+│ │ 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] │
+└───┴─────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -349,20 +350,20 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-131
+Tag: RandomForestRegressor-528
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
-│ A │ LPLoss( │ predict │ 2.429 │ 0.095 │
+│ A │ LPLoss( │ predict │ 2.48 │ 0.12 │
│ │ p = 1) │ │ │ │
-│ B │ RSquared() │ predict │ 0.84 │ 0.012 │
+│ B │ RSquared() │ predict │ 0.826 │ 0.021 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬───────────────────────────────────────────────────────────────────────┐
│ │ per_fold │
├───┼───────────────────────────────────────────────────────────────────────┤
-│ A │ [2.29, 2.52, 2.51, 2.33, 2.41, 2.52, 2.52, 2.25, 2.26, 2.68] │
-│ B │ [0.825, 0.853, 0.841, 0.851, 0.83, 0.846, 0.853, 0.858, 0.851, 0.795] │
+│ 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] │
└───┴───────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -373,8 +374,8 @@ e.uncertainty_radius_95
````
2-element Vector{Float64}:
- 0.09488695112138384
- 0.012422766062865595
+ 0.12150183577386904
+ 0.02074147391875972
````
# Interlude on scientific types
@@ -619,8 +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=>0.9, >50K=>0.1)
- UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.25, >50K=>0.75)
+ 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=>1.0, >50K=>0.0)
````
@@ -631,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
- <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.9
- >50K ┤■■■■ 0.1
+ <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
+ >50K ┤■■■■ 0.11
└ ┘
````
@@ -643,7 +644,7 @@ accuracy(ypoint, y[test])
````
````
-0.7817986384808312
+0.7822081179300814
````
````@julia
@@ -651,7 +652,7 @@ log_loss(yprob, y[test])
````
````
-1.532945976938586
+1.6055684252777864
````
Evaluate with one command:
@@ -671,13 +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-494
+Tag: RandomForestClassifier-917
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
-│ Accuracy() │ predict_mode │ 0.782 │
-│ LogLoss( │ predict │ 1.53 │
+│ Accuracy() │ predict_mode │ 0.781 │
+│ LogLoss( │ predict │ 1.58 │
│ 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 2162e5c..9aabbcb 100644
--- a/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
@@ -6,6 +6,7 @@ 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.
To run the code in this tutorial in a live Julia session, first follow the instructions
@@ -159,7 +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-776
+Tag: DeterministicPipeline-790
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -224,7 +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-712
+Tag: TransformedTargetModelDeterministic-136
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 7df8fcb..c57796b 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 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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diff --git a/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md b/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
index ea90790..dd12ac0 100644
--- a/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
@@ -6,6 +6,7 @@ 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.
To run the code in this tutorial in a live Julia session, first follow the instructions
@@ -93,7 +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-772
+Tag: ProbabilisticPipeline-299
Extract:
┌──────────┬──────────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -160,7 +161,7 @@ savefig("learning_curve.svg");
````
[ Info: Training machine(DeterministicTunedModel(model = DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …), …).
[ Info: Attempting to evaluate 30 models.
-
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+
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````
@@ -237,7 +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.
-
Evaluating over 36 metamodels: 6%[=> ] ETA: 0:00:47[K
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+
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````
@@ -319,7 +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.
-
Evaluating over 40 metamodels: 5%[=> ] ETA: 0:00:57[K
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````
@@ -353,7 +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-147
+Tag: DeterministicTunedModel-572
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -376,9 +377,9 @@ Comparing with the baseline computed earlier:
````
````
-ebase = PerformanceEvaluation("ProbabilisticPipeline-772", 0.568 ± 0.033)
-e0 = PerformanceEvaluation("DeterministicPipeline-751", 0.184 ± 0.028)
-e1 = PerformanceEvaluation("DeterministicTunedModel-147", 0.175 ± 0.021)
+ebase = PerformanceEvaluation("ProbabilisticPipeline-299", 0.568 ± 0.033)
+e0 = PerformanceEvaluation("DeterministicPipeline-781", 0.184 ± 0.028)
+e1 = PerformanceEvaluation("DeterministicTunedModel-572", 0.175 ± 0.021)
````
@@ -394,8 +395,8 @@ describe.([e0, e1]) |> pretty
│ String │ Measurement{Float64} │
│ Textual │ Continuous │
├─────────────────────────────┼──────────────────────┤
-│ DeterministicPipeline-751 │ 0.184±0.028 │
-│ DeterministicTunedModel-147 │ 0.175±0.021 │
+│ DeterministicPipeline-781 │ 0.184±0.028 │
+│ DeterministicTunedModel-572 │ 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 61ea8e7..09128de 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 eb5cc66..90cf847 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 6ef948d..7506735 100644
--- a/learning_curve.svg
+++ b/learning_curve.svg
@@ -1,46 +1,46 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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diff --git a/tuned_model_grid.svg b/tuned_model_grid.svg
index b4fab86..358713d 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 3233ede..45f3076 100644
--- a/tuned_model_random.svg
+++ b/tuned_model_random.svg
@@ -1,269 +1,269 @@