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ablaom merged 1 commit into from
Oct 17, 2026
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Commit Notebook Markdown Changes #101

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58 changes: 29 additions & 29 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 @@ -199,8 +199,8 @@ fitted_params(mach)
````
(forest = Ensemble of Decision Trees
Trees: 100
Avg Leaves: 146.66
Avg Depth: 14.83,)
Avg Leaves: 146.89
Avg Depth: 14.44,)
````

````@julia
Expand All @@ -219,9 +219,9 @@ predict(mach, X)[1:3]

````
3-element Vector{Float64}:
26.535999999999998
22.762999999999998
34.841
26.087
23.107999999999997
34.562
````

Predict in the `test` rows:
Expand All @@ -237,7 +237,7 @@ mae(ypred, y[test])
````

````
4.457232673267327
4.429029702970296
````

`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-797
Tag: RandomForestRegressor-530
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 4.46 │
│ LPLoss( │ predict │ 4.43 │
│ p = 1) │ │ │
│ RSquared() │ predict │ 0.344 │
│ RSquared() │ predict │ 0.351 │
└────────────┴───────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand All @@ -313,20 +313,20 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-427
Tag: RandomForestRegressor-360
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 2.99 │ 0.87 │
│ A │ LPLoss( │ predict │ 2.99 │ 0.85 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.66 │ 0.16 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬────────────────────────────────────────────┐
│ │ per_fold │
├───┼────────────────────────────────────────────┤
│ A │ [2.24, 2.23, 3.34, 2.43, 4.81, 2.92] │
│ B │ [0.721, 0.839, 0.722, 0.828, 0.448, 0.408] │
│ A │ [2.2, 2.28, 3.27, 2.54, 4.81, 2.87] │
│ B │ [0.735, 0.832, 0.731, 0.794, 0.461, 0.385] │
└───┴────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand All @@ -350,20 +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-298
Tag: RandomForestRegressor-398
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 2.46 │ 0.11 │
│ A │ LPLoss( │ predict │ 2.42 │ 0.13 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.821 │ 0.027 │
│ B │ RSquared() │ predict │ 0.844 │ 0.02 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬────────────────────────────────────────────────────────────────────────┐
│ │ per_fold │
├───┼────────────────────────────────────────────────────────────────────────┤
│ A │ [2.46, 2.51, 2.61, 2.23, 2.5, 2.42, 2.7, 2.3, 2.22, 2.67] │
│ B │ [0.757, 0.859, 0.808, 0.863, 0.775, 0.854, 0.789, 0.842, 0.869, 0.791] │
│ A │ [2.29, 2.77, 2.38, 2.27, 2.25, 2.57, 2.28, 2.6, 2.57, 2.22] │
│ B │ [0.851, 0.799, 0.859, 0.867, 0.827, 0.847, 0.889, 0.791, 0.836, 0.872] │
└───┴────────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand All @@ -374,8 +374,8 @@ e.uncertainty_radius_95

````
2-element Vector{Float64}:
0.11330666259443022
0.02705219873485344
0.1253300607195079
0.020432947940809142
````

# 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=>0.9, >50K=>0.1)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.29, >50K=>0.71)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.94, >50K=>0.06)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.33, >50K=>0.67)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
````

Expand All @@ -632,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.9
>50K ┤■■■■ 0.1
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.94
>50K ┤■■ 0.06
└ ┘
````

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

````
0.7819521932743001
0.7830270768285816
````

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

````
1.5469870467696316
1.6401273020819653
````

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-165
Tag: RandomForestClassifier-818
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
│ Accuracy() │ predict_mode │ 0.78 │
│ LogLoss( │ predict │ 1.54 │
│ Accuracy() │ predict_mode │ 0.781 │
│ LogLoss( │ predict │ 1.56 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴──────────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
Expand Down
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