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ablaom merged 1 commit into from
Sep 29, 2026
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Commit Notebook Markdown Changes #75

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10 changes: 5 additions & 5 deletions docs/src/notebooks/MLJTutorial/01_data_representation/notebook.md
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Expand Up @@ -290,7 +290,7 @@ csv_file = Downloads.download(url)
````

````
"/tmp/jl_nAeUTc/horse.csv"
"/tmp/jl_ruz0kP/horse.csv"
````

Entering these lines of code downloads the data to a temporary file at the location
Expand Down Expand Up @@ -531,8 +531,8 @@ A = rand(2, 3)

````
2×3 Matrix{Float64}:
0.84235 0.324562 0.731728
0.233998 0.746378 0.485095
0.113155 0.33251 0.397839
0.171581 0.90293 0.436722
````

````@julia
Expand All @@ -550,8 +550,8 @@ Asparse = sparse(A)

````
2×3 SparseArrays.SparseMatrixCSC{Float64, Int64} with 6 stored entries:
0.84235 0.324562 0.731728
0.233998 0.746378 0.485095
0.113155 0.33251 0.397839
0.171581 0.90293 0.436722
````

````@julia
Expand Down
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313 changes: 158 additions & 155 deletions docs/src/notebooks/MLJTutorial/02_models/notebook.md
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100 changes: 52 additions & 48 deletions docs/src/notebooks/MLJTutorial/03_pipelines/notebook.md
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Expand Up @@ -31,8 +31,8 @@ x = rand(100);
````

````
mean(x) = 0.52573272422357
std(x) = 0.3020501201265377
mean(x) = 0.47696749924138343
std(x) = 0.29728842148044776

````

Expand All @@ -46,7 +46,7 @@ xhat = transform(mach, x);

````
[ Info: Training machine(Standardizer(features = Symbol[], …), …).
mean(xhat) = -1.8596235662471373e-16
mean(xhat) = 2.042810365310288e-16
std(xhat) = 1.0

````
Expand Down Expand Up @@ -497,18 +497,19 @@ evaluate!(mach, measure=mae, resampling=Holdout()) # `CV(nfolds=6)` is `resampli
````
PerformanceEvaluation object with these fields:
model, tag, measure, operation,
measurement, uncertainty_radius_95, per_fold, per_observation,
measurement (per-fold aggregate), uncertainty_radius_95 (1.96*SE),
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-661
Tag: DeterministicPipeline-190
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 176000.0 │
│ p = 1) │ │ │
└──────────┴───────────┴─────────────┘

Apply `describe` to this result for a named tuple summary.
````

### Training of composite models is "smart"
Expand Down Expand Up @@ -626,23 +627,24 @@ evaluate!(mach, measure=mae)
````
PerformanceEvaluation object with these fields:
model, tag, measure, operation,
measurement, uncertainty_radius_95, per_fold, per_observation,
measurement (per-fold aggregate), uncertainty_radius_95 (1.96*SE),
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-106
Tag: DeterministicPipeline-398
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 162000.0 │
│ p = 1) │ │ │
└──────────┴───────────┴─────────────┘
┌──────────────────────────────────────────────────────────────┬─────────┐
│ per_fold │ 1.96*SE │
├──────────────────────────────────────────────────────────────┼─────────┤
│ [160000.0, 170000.0, 163000.0, 156000.0, 163000.0, 162000.0] │ 4140.0 │
└──────────────────────────────────────────────────────────────┴─────────┘

┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
├──────────┼───────────┼─────────────┼─────────┤
│ LPLoss( │ predict │ 162300.0 │ 4100.0 │
│ p = 1) │ │ │ │
└──────────┴───────────┴─────────────┴─────────┘
┌──────────────────────────────────────────────────────────────┐
│ per_fold │
├──────────────────────────────────────────────────────────────┤
│ [160000.0, 170000.0, 163000.0, 156000.0, 163000.0, 162000.0] │
└──────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

MLJ will also allow you to insert *learned* target transformations. For example, we
Expand All @@ -666,23 +668,24 @@ evaluate!(mach, measure=mae)
````
PerformanceEvaluation object with these fields:
model, tag, measure, operation,
measurement, uncertainty_radius_95, per_fold, per_observation,
measurement (per-fold aggregate), uncertainty_radius_95 (1.96*SE),
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-264
Tag: DeterministicPipeline-611
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 509000.0 │
│ p = 1) │ │ │
└──────────┴───────────┴─────────────┘
┌───────────────────────────────────────────────────────────┬──────────┐
│ per_fold │ 1.96*SE │
├───────────────────────────────────────────────────────────┼──────────┤
│ [162000.0, 2.2e6, 181000.0, 161000.0, 176000.0, 172000.0] │ 728000.0 │
└───────────────────────────────────────────────────────────┴──────────┘

┌──────────┬───────────┬─────────────┬──────────┐
│ measure │ operation │ measurement │ 1.96*SE │
├──────────┼───────────┼─────────────┼──────────┤
│ LPLoss( │ predict │ 510000.0 │ 730000.0 │
│ p = 1) │ │ │ │
└──────────┴───────────┴─────────────┴──────────┘
┌───────────────────────────────────────────────────────────┐
│ per_fold │
├───────────────────────────────────────────────────────────┤
│ [162000.0, 2.2e6, 181000.0, 161000.0, 176000.0, 172000.0] │
└───────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

````@julia
Expand All @@ -693,23 +696,24 @@ evaluate!(mach, measure=mae)
````
PerformanceEvaluation object with these fields:
model, tag, measure, operation,
measurement, uncertainty_radius_95, per_fold, per_observation,
measurement (per-fold aggregate), uncertainty_radius_95 (1.96*SE),
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-300
Tag: DeterministicPipeline-592
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 172000.0 │
│ p = 1) │ │ │
└──────────┴───────────┴─────────────┘
┌──────────────────────────────────────────────────────────────┬─────────┐
│ per_fold │ 1.96*SE │
├──────────────────────────────────────────────────────────────┼─────────┤
│ [171000.0, 172000.0, 173000.0, 170000.0, 173000.0, 171000.0] │ 1240.0 │
└──────────────────────────────────────────────────────────────┴─────────┘

┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
├──────────┼───────────┼─────────────┼─────────┤
│ LPLoss( │ predict │ 171500.0 │ 1200.0 │
│ p = 1) │ │ │ │
└──────────┴───────────┴─────────────┴─────────┘
┌──────────────────────────────────────────────────────────────┐
│ per_fold │
├──────────────────────────────────────────────────────────────┤
│ [171000.0, 172000.0, 173000.0, 170000.0, 173000.0, 171000.0] │
└──────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

### Tutorial 3 Resources
Expand Down
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58 changes: 30 additions & 28 deletions docs/src/notebooks/MLJTutorial/04_tuning/notebook.md
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Expand Up @@ -363,23 +363,24 @@ err = evaluate!(mach, resampling=CV(nfolds=3), measure=log_loss)
````
PerformanceEvaluation object with these fields:
model, tag, measure, operation,
measurement, uncertainty_radius_95, per_fold, per_observation,
measurement (per-fold aggregate), uncertainty_radius_95 (1.96*SE),
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: ProbabilisticPipeline-509
Tag: ProbabilisticPipeline-307
Extract:
┌──────────────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼───────────┼─────────────┤
│ LogLoss( │ predict │ 0.782 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴───────────┴─────────────┘
┌──────────────────────┬─────────┐
│ per_fold │ 1.96*SE │
├──────────────────────┼─────────┤
│ [0.83, 0.721, 0.794] │ 0.0773 │
└──────────────────────┴─────────┘

┌──────────────────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
├──────────────────────┼───────────┼─────────────┼─────────┤
│ LogLoss( │ predict │ 0.782 │ 0.077 │
│ tol = 2.22045e-16) │ │ │ │
└──────────────────────┴───────────┴─────────────┴─────────┘
┌──────────────────────┐
│ per_fold │
├──────────────────────┤
│ [0.83, 0.721, 0.794] │
└──────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

````@julia
Expand All @@ -389,23 +390,24 @@ tuned_err = evaluate!(tuned_mach, resampling=CV(nfolds=3), measure=log_loss)
````
PerformanceEvaluation object with these fields:
model, tag, measure, operation,
measurement, uncertainty_radius_95, per_fold, per_observation,
measurement (per-fold aggregate), uncertainty_radius_95 (1.96*SE),
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: ProbabilisticTunedModel-395
Tag: ProbabilisticTunedModel-779
Extract:
┌──────────────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼───────────┼─────────────┤
│ LogLoss( │ predict │ 0.779 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴───────────┴─────────────┘
┌───────────────────────┬─────────┐
│ per_fold │ 1.96*SE │
├───────────────────────┼─────────┤
│ [0.798, 0.802, 0.738] │ 0.0496 │
└───────────────────────┴─────────┘

┌──────────────────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
├──────────────────────┼───────────┼─────────────┼─────────┤
│ LogLoss( │ predict │ 0.779 │ 0.05 │
│ tol = 2.22045e-16) │ │ │ │
└──────────────────────┴───────────┴─────────────┴─────────┘
┌───────────────────────┐
│ per_fold │
├───────────────────────┤
│ [0.798, 0.802, 0.738] │
└───────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

### Tutorial 4 Resources
Expand Down
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