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ablaom merged 1 commit into from
Oct 16, 2026
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Commit Notebook Markdown Changes #97

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80 changes: 40 additions & 40 deletions docs/src/notebooks/UsingMLJ/01_basics/notebook.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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).
Expand Down Expand Up @@ -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
Expand All @@ -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:
Expand All @@ -237,7 +237,7 @@ mae(ypred, y[test])
````

````
4.639163366336634
4.776490099009901
````

`mae` is actually just an alias:
Expand Down Expand Up @@ -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.
````
Expand All @@ -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.
````

Expand All @@ -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.
````

Expand All @@ -374,8 +374,8 @@ e.uncertainty_radius_95

````
2-element Vector{Float64}:
0.12150183577386904
0.02074147391875972
0.18403789695002817
0.04574117004868558
````

# Interlude on scientific types
Expand Down Expand Up @@ -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)
````

Expand All @@ -632,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
>50K ┤■■■■ 0.11
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.97
>50K ┤■ 0.03
└ ┘
````

Expand All @@ -644,15 +644,15 @@ accuracy(ypoint, y[test])
````

````
0.7822081179300814
0.7799047960280493
````

````@julia
log_loss(yprob, y[test])
````

````
1.6055684252777864
1.5392550897855832
````

Evaluate with one command:
Expand All @@ -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.
Expand Down
8 changes: 4 additions & 4 deletions docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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).
Expand Down Expand Up @@ -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 │
Expand Down Expand Up @@ -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 │
Expand Down
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