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ablaom merged 1 commit into from
Aug 28, 2026
Merged

Commit Notebook Markdown Changes #67

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

````
"/tmp/jl_7hCVl9/horse.csv"
"/tmp/jl_AutsNd/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}:
0.68852 0.626316 0.486213
0.206646 0.0678205 0.909788
0.56044 0.53664 0.852799
0.83311 0.655101 0.199531
````

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

````
2×3 SparseArrays.SparseMatrixCSC{Float64, Int64} with 6 stored entries:
0.68852 0.626316 0.486213
0.206646 0.0678205 0.909788
0.56044 0.53664 0.852799
0.83311 0.655101 0.199531
````

````@julia
Expand Down
272 changes: 136 additions & 136 deletions docs/src/notebooks/MLJTutorial/02_models/notebook.md
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22 changes: 11 additions & 11 deletions docs/src/notebooks/MLJTutorial/03_pipelines/notebook.md
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Expand Up @@ -31,8 +31,8 @@ x = rand(100);
````

````
mean(x) = 0.5049271509171457
std(x) = 0.2930352399455027
mean(x) = 0.48994182660190133
std(x) = 0.3012292457201098

````

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

````
[ Info: Training machine(Standardizer(features = Symbol[], …), …).
mean(xhat) = -1.9095836023552692e-16
mean(xhat) = -9.103828801926283e-17
std(xhat) = 1.0

````
Expand Down Expand Up @@ -500,7 +500,7 @@ PerformanceEvaluation object with these fields:
measurement, uncertainty_radius_95, per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-586
Tag: DeterministicPipeline-366
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand Down Expand Up @@ -537,10 +537,10 @@ fit!(mach);
````

````
[ Info: Updating machine(DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …).
[ Info: Updating machine(DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …) (with warm restart if possible).
[ Info: Not retraining machine(:continuous_encoder, …). Use `force=true` to force.
[ Info: Not retraining machine(:pca, …). Use `force=true` to force.
[ Info: Updating machine(:ridge_regressor, …).
[ Info: Updating machine(:ridge_regressor, …) (with warm restart if possible).
┌ Info: Solver: MLJLinearModels.Analytical
│ iterative: Bool false
└ max_inner: Int64 200
Expand All @@ -558,9 +558,9 @@ fit!(mach);
````

````
[ Info: Updating machine(DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …).
[ Info: Updating machine(DeterministicPipeline(continuous_encoder = ContinuousEncoder(drop_last = false, …), …), …) (with warm restart if possible).
[ Info: Not retraining machine(:continuous_encoder, …). Use `force=true` to force.
[ Info: Updating machine(:pca, …).
[ Info: Updating machine(:pca, …) (with warm restart if possible).
[ Info: Training machine(:ridge_regressor, …).
┌ Info: Solver: MLJLinearModels.Analytical
│ iterative: Bool false
Expand Down Expand Up @@ -629,7 +629,7 @@ PerformanceEvaluation object with these fields:
measurement, uncertainty_radius_95, per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-488
Tag: DeterministicPipeline-710
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand Down Expand Up @@ -669,7 +669,7 @@ PerformanceEvaluation object with these fields:
measurement, uncertainty_radius_95, per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-307
Tag: DeterministicPipeline-846
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand All @@ -696,7 +696,7 @@ PerformanceEvaluation object with these fields:
measurement, uncertainty_radius_95, per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-882
Tag: DeterministicPipeline-649
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand Down
15 changes: 9 additions & 6 deletions docs/src/notebooks/MLJTutorial/04_tuning/notebook.md
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Expand Up @@ -144,7 +144,7 @@ savefig("learning_curve2.png")
````

````
"/home/runner/work/MLJTutorial.jl/MLJTutorial.jl/docs/src/notebooks/04_tuning/learning_curve2.png"
"/home/runner/work/MLJTutorial.jl/MLJTutorial.jl/docs/src/notebooks/MLJTutorial/04_tuning/learning_curve2.png"
````

![](learning_curve2.png)
Expand Down Expand Up @@ -366,7 +366,7 @@ PerformanceEvaluation object with these fields:
measurement, uncertainty_radius_95, per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: ProbabilisticPipeline-133
Tag: ProbabilisticPipeline-992
Extract:
┌──────────────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand All @@ -392,7 +392,7 @@ PerformanceEvaluation object with these fields:
measurement, uncertainty_radius_95, per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: ProbabilisticTunedModel-228
Tag: ProbabilisticTunedModel-915
Extract:
┌──────────────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand Down Expand Up @@ -487,6 +487,7 @@ DeterministicPipeline(
nrounds = 70,
bagging_size = 1,
early_stopping_rounds = 9223372036854775807,
early_stopping_tolerance = 0.0,
L2 = 1.0,
lambda = 0.0,
gamma = 0.0,
Expand All @@ -506,18 +507,20 @@ DeterministicPipeline(
````

(a) Construct a bounded range `r1` for the `evo_tree_booster`
parameter `max_depth`, varying between 1 and 12.
parameter `max_depth`, varying between 4 and 14.

(b) For the `colsample` parameter of the `EvoTreeRegressor`, define the range

````@julia
r2 = range(model, :(evo_tree_regressor.colsample), lower=0.5, upper=1.0)
r2 = range(model, :(evo_tree_regressor.nbins), values = [32, 64, 128])
````

````
NumericRange(0.5 ≤ evo_tree_regressor.colsample ≤ 1.0; origin=0.75, unit=0.25)
NominalRange(evo_tree_regressor.nbins = 32, 64, 128)
````

(a)

Optimize `model` over these the parameter ranges `r1` and `r2` using a random search
with uniform priors (the default). Use `Holdout()` resampling, and implement your search
by first constructing a "self-tuning" wrap of `model`, as described above. Make `mae`
Expand Down
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