From 282c18d0893f718513ed6086764f1a07703bd288 Mon Sep 17 00:00:00 2001
From: ablaom <30517088+ablaom@users.noreply.github.com>
Date: Thu, 8 Oct 2026 21:13:33 +0000
Subject: [PATCH] update notebook markdown
---
.../notebooks/UsingMLJ/01_basics/notebook.md | 87 ++-
.../UsingMLJ/02_model_composition/notebook.md | 23 +-
.../03_model_tuning/learning_curve.svg | 64 +--
.../UsingMLJ/03_model_tuning/notebook.md | 37 +-
.../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, 1162 insertions(+), 1165 deletions(-)
diff --git a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
index 662e437..ecef8aa 100644
--- a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md
@@ -14,14 +14,13 @@ given [here](@ref instructions).
### Video Timings
- 00:00 Introduction
-- 01:05 Goals
-- 01:48 Prerequisites and Getting Help
-- 02:21 Supervised Learning Recap
-- 05:31 Models and Machines
-- 09:28 **Live Coding:** Regression
-- 28:55 Scientific Types
-- 32:29 **Live Coding:** Scitypes and Classification
-- 55:45 End
+- 01:10 Goals
+- 01:57 Prerequisites and Getting Help
+- 02:33 Supervised Learning Recap
+- 05:48 Models and Machines
+- 09:49 **Live Coding:** Regression
+- 29:20 Scientific Types
+- 32:52 **Live Coding:** Scitypes and Classification
We start by inspecting the packages, and their exact versions, in the currently active
package environment:
@@ -199,8 +198,8 @@ fitted_params(mach)
````
(forest = Ensemble of Decision Trees
Trees: 100
-Avg Leaves: 146.21
-Avg Depth: 14.61,)
+Avg Leaves: 146.59
+Avg Depth: 14.81,)
````
````@julia
@@ -219,9 +218,9 @@ predict(mach, X)[1:3]
````
3-element Vector{Float64}:
- 27.011
- 22.410999999999994
- 34.27
+ 25.982999999999997
+ 22.639
+ 34.983999999999995
````
Predict in the `test` rows:
@@ -237,7 +236,7 @@ mae(ypred, y[test])
````
````
-4.574237623762377
+4.5643613861386125
````
`mae` is actually just an alias:
@@ -284,14 +283,14 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-336
+Tag: RandomForestRegressor-156
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
-│ LPLoss( │ predict │ 4.57 │
+│ LPLoss( │ predict │ 4.56 │
│ p = 1) │ │ │
-│ RSquared() │ predict │ 0.33 │
+│ RSquared() │ predict │ 0.326 │
└────────────┴───────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -313,21 +312,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-817
+Tag: RandomForestRegressor-481
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
-│ A │ LPLoss( │ predict │ 3.05 │ 0.89 │
+│ A │ LPLoss( │ predict │ 2.99 │ 0.87 │
│ │ p = 1) │ │ │ │
-│ B │ RSquared() │ predict │ 0.65 │ 0.16 │
+│ B │ RSquared() │ predict │ 0.67 │ 0.15 │
└───┴────────────┴───────────┴─────────────┴─────────┘
-┌───┬─────────────────────────────────────────┐
-│ │ per_fold │
-├───┼─────────────────────────────────────────┤
-│ A │ [2.27, 2.36, 3.25, 2.48, 4.97, 2.96] │
-│ B │ [0.723, 0.81, 0.742, 0.806, 0.441, 0.4] │
-└───┴─────────────────────────────────────────┘
+┌───┬──────────────────────────────────────────┐
+│ │ per_fold │
+├───┼──────────────────────────────────────────┤
+│ A │ [2.25, 2.34, 3.23, 2.45, 4.89, 2.81] │
+│ B │ [0.72, 0.831, 0.732, 0.82, 0.434, 0.459] │
+└───┴──────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -350,21 +349,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestRegressor-621
+Tag: RandomForestRegressor-120
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
-│ A │ LPLoss( │ predict │ 2.48 │ 0.1 │
+│ A │ LPLoss( │ predict │ 2.436 │ 0.085 │
│ │ p = 1) │ │ │ │
-│ B │ RSquared() │ predict │ 0.817 │ 0.038 │
+│ B │ RSquared() │ predict │ 0.84 │ 0.022 │
└───┴────────────┴───────────┴─────────────┴─────────┘
-┌───┬──────────────────────────────────────────────────────────────────────┐
-│ │ per_fold │
-├───┼──────────────────────────────────────────────────────────────────────┤
-│ A │ [2.37, 2.74, 2.27, 2.42, 2.75, 2.54, 2.36, 2.48, 2.41, 2.5] │
-│ B │ [0.87, 0.702, 0.857, 0.848, 0.824, 0.72, 0.843, 0.834, 0.839, 0.834] │
-└───┴──────────────────────────────────────────────────────────────────────┘
+┌───┬────────────────────────────────────────────────────────────────────────┐
+│ │ per_fold │
+├───┼────────────────────────────────────────────────────────────────────────┤
+│ A │ [2.41, 2.46, 2.57, 2.36, 2.45, 2.39, 2.18, 2.55, 2.64, 2.36] │
+│ B │ [0.848, 0.846, 0.836, 0.855, 0.819, 0.857, 0.865, 0.847, 0.757, 0.874] │
+└───┴────────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
@@ -374,8 +373,8 @@ e.uncertainty_radius_95
````
2-element Vector{Float64}:
- 0.10349121844149421
- 0.03773282475420102
+ 0.08509364727613682
+ 0.021656217946986845
````
# Interlude on scientific types
@@ -620,7 +619,7 @@ 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=>0.9, >50K=>0.1)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.28, >50K=>0.72)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
````
@@ -632,8 +631,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
- <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
- >50K ┤■■■■ 0.11
+ <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.9
+ >50K ┤■■■■ 0.1
└ ┘
````
@@ -644,7 +643,7 @@ accuracy(ypoint, y[test])
````
````
-0.7821057480677689
+0.7809796795823309
````
````@julia
@@ -652,7 +651,7 @@ log_loss(yprob, y[test])
````
````
-1.53133125245395
+1.5800432929507295
````
Evaluate with one command:
@@ -672,13 +671,13 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: RandomForestClassifier-321
+Tag: RandomForestClassifier-844
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
│ Accuracy() │ predict_mode │ 0.782 │
-│ LogLoss( │ predict │ 1.55 │
+│ LogLoss( │ predict │ 1.52 │
│ 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 7dde1c6..c0334e7 100644
--- a/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
@@ -14,16 +14,15 @@ given [here](@ref instructions).
### Video Timings
- 00:00 Introduction
-- 00:08 Goals
-- 01:19 Prerequisites and Getting Help
-- 02:00 Composite Models Defined
-- 04:13 Model Pipelines
-- 05:59 Data Leakage
-- 08:37 Target Transformations
-- 12:43 Live Coding: Pipelines
-- 18:42 Live Coding: Target Transformations
-- 20:35 Other Model Wrappers
-- 25:02 End
+- 00:13 Goals
+- 01:28 Prerequisites and Getting Help
+- 02:12 Composite Models Defined
+- 04:28 Model Pipelines
+- 06:07 Data Leakage
+- 09:00 Target Transformations
+- 13:10 **Live Coding:** Pipelines
+- 19:11 **Live Coding:** Target Transformations
+- 21:01 Other Model Wrappers
````@julia
using MLJ
@@ -160,7 +159,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: DeterministicPipeline-500
+Tag: DeterministicPipeline-303
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -225,7 +224,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: TransformedTargetModelDeterministic-470
+Tag: TransformedTargetModelDeterministic-135
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 1f9c598..1791834 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 a6661c2..fdf821b 100644
--- a/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
+++ b/docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.md
@@ -14,15 +14,14 @@ given [here](@ref instructions).
### Video Timings
- 00:00 Introduction
-- 00:06 Goals
-- 01:12 Prerequisites and Getting Help
-- 01:27 Live Coding: Learning Curves
-- 13:27 Tuning as Model Wrapper
-- 18:45 Live Coding: Grid Search
-- 26:17 Live Coding: Random Search
-- 30:48 Nested Resampling
-- 33:11 Final Observations
-- 36:59 End
+- 00:11 Goals
+- 01:21 Prerequisites and Getting Help
+- 01:42 **Live Coding:** Learning Curves
+- 13:57 Tuning as Model Wrapper
+- 18:53 **Live Coding:** Grid Search
+- 26:32 **Live Coding:** Random Search
+- 31:07 Nested Resampling
+- 33:31 Final Observations
## Part I. Learning Curves
@@ -94,7 +93,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: ProbabilisticPipeline-489
+Tag: ProbabilisticPipeline-745
Extract:
┌──────────┬──────────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -161,7 +160,7 @@ 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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````
@@ -238,7 +237,7 @@ 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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````
@@ -320,7 +319,7 @@ 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: 0%[> ] ETA: N/A[K
Evaluating over 40 metamodels: 2%[> ] ETA: 0:00:52[K
Evaluating over 40 metamodels: 5%[=> ] ETA: 0:00:50[K
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Evaluating over 40 metamodels: 25%[======> ] ETA: 0:00:39[K
Evaluating over 40 metamodels: 28%[======> ] ETA: 0:00:36[K
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Evaluating over 40 metamodels: 58%[==============> ] ETA: 0:00:22[K
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Evaluating over 40 metamodels: 88%[=====================> ] ETA: 0:00:06[K
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Evaluating over 40 metamodels: 92%[=======================> ] ETA: 0:00:04[K
Evaluating over 40 metamodels: 95%[=======================> ] ETA: 0:00:02[K
Evaluating over 40 metamodels: 98%[========================>] ETA: 0:00:01[K
Evaluating over 40 metamodels: 100%[=========================] Time: 0:00:50[K
+
Evaluating over 40 metamodels: 0%[> ] ETA: N/A[K
Evaluating over 40 metamodels: 2%[> ] ETA: 0:00:33[K
Evaluating over 40 metamodels: 5%[=> ] ETA: 0:00:45[K
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Evaluating over 40 metamodels: 10%[==> ] ETA: 0:00:50[K
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Evaluating over 40 metamodels: 28%[======> ] ETA: 0:00:40[K
Evaluating over 40 metamodels: 30%[=======> ] ETA: 0:00:41[K
Evaluating over 40 metamodels: 32%[========> ] ETA: 0:00:39[K
Evaluating over 40 metamodels: 35%[========> ] ETA: 0:00:38[K
Evaluating over 40 metamodels: 38%[=========> ] ETA: 0:00:36[K
Evaluating over 40 metamodels: 40%[==========> ] ETA: 0:00:34[K
Evaluating over 40 metamodels: 42%[==========> ] ETA: 0:00:35[K
Evaluating over 40 metamodels: 45%[===========> ] ETA: 0:00:33[K
Evaluating over 40 metamodels: 48%[===========> ] ETA: 0:00:31[K
Evaluating over 40 metamodels: 50%[============> ] ETA: 0:00:30[K
Evaluating over 40 metamodels: 52%[=============> ] ETA: 0:00:28[K
Evaluating over 40 metamodels: 55%[=============> ] ETA: 0:00:27[K
Evaluating over 40 metamodels: 58%[==============> ] ETA: 0:00:25[K
Evaluating over 40 metamodels: 60%[===============> ] ETA: 0:00:24[K
Evaluating over 40 metamodels: 62%[===============> ] ETA: 0:00:23[K
Evaluating over 40 metamodels: 65%[================> ] ETA: 0:00:21[K
Evaluating over 40 metamodels: 68%[================> ] ETA: 0:00:19[K
Evaluating over 40 metamodels: 70%[=================> ] ETA: 0:00:18[K
Evaluating over 40 metamodels: 72%[==================> ] ETA: 0:00:16[K
Evaluating over 40 metamodels: 75%[==================> ] ETA: 0:00:15[K
Evaluating over 40 metamodels: 78%[===================> ] ETA: 0:00:13[K
Evaluating over 40 metamodels: 80%[====================> ] ETA: 0:00:12[K
Evaluating over 40 metamodels: 82%[====================> ] ETA: 0:00:10[K
Evaluating over 40 metamodels: 85%[=====================> ] ETA: 0:00:09[K
Evaluating over 40 metamodels: 88%[=====================> ] ETA: 0:00:07[K
Evaluating over 40 metamodels: 90%[======================> ] ETA: 0:00:06[K
Evaluating over 40 metamodels: 92%[=======================> ] ETA: 0:00:04[K
Evaluating over 40 metamodels: 95%[=======================> ] ETA: 0:00:03[K
Evaluating over 40 metamodels: 98%[========================>] ETA: 0:00:01[K
Evaluating over 40 metamodels: 100%[=========================] Time: 0:00:58[K
````
@@ -354,7 +353,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
-Tag: DeterministicTunedModel-587
+Tag: DeterministicTunedModel-560
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
@@ -377,9 +376,9 @@ Comparing with the baseline computed earlier:
````
````
-ebase = PerformanceEvaluation("ProbabilisticPipeline-489", 0.568 ± 0.033)
-e0 = PerformanceEvaluation("DeterministicPipeline-381", 0.184 ± 0.028)
-e1 = PerformanceEvaluation("DeterministicTunedModel-587", 0.175 ± 0.021)
+ebase = PerformanceEvaluation("ProbabilisticPipeline-745", 0.568 ± 0.033)
+e0 = PerformanceEvaluation("DeterministicPipeline-862", 0.184 ± 0.028)
+e1 = PerformanceEvaluation("DeterministicTunedModel-560", 0.175 ± 0.021)
````
@@ -395,8 +394,8 @@ describe.([e0, e1]) |> pretty
│ String │ Measurement{Float64} │
│ Textual │ Continuous │
├─────────────────────────────┼──────────────────────┤
-│ DeterministicPipeline-381 │ 0.184±0.028 │
-│ DeterministicTunedModel-587 │ 0.175±0.021 │
+│ DeterministicPipeline-862 │ 0.184±0.028 │
+│ DeterministicTunedModel-560 │ 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 b284d62..bea1e11 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 fdc05dd..9f4f65b 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 177bf45..93af590 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 94c8ce2..7276deb 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 5f65b2d..9fb4694 100644
--- a/tuned_model_random.svg
+++ b/tuned_model_random.svg
@@ -1,269 +1,269 @@