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ablaom merged 1 commit into from
Oct 16, 2026
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Commit Notebook Markdown Changes #95

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69 changes: 35 additions & 34 deletions docs/src/notebooks/UsingMLJ/01_basics/notebook.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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:
Expand All @@ -236,7 +237,7 @@ mae(ypred, y[test])
````

````
4.458702970297029
4.639163366336634
````

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

Expand All @@ -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.
````
Expand All @@ -373,8 +374,8 @@ e.uncertainty_radius_95

````
2-element Vector{Float64}:
0.09488695112138384
0.012422766062865595
0.12150183577386904
0.02074147391875972
````

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

Expand All @@ -631,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.9
>50K ┤■■■■ 0.1
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
>50K ┤■■■■ 0.11
└ ┘
````

Expand All @@ -643,15 +644,15 @@ accuracy(ypoint, y[test])
````

````
0.7817986384808312
0.7822081179300814
````

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

````
1.532945976938586
1.6055684252777864
````

Evaluate with one command:
Expand All @@ -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.
Expand Down
5 changes: 3 additions & 2 deletions docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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 │
Expand Down Expand Up @@ -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 │
Expand Down
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