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60 changes: 30 additions & 30 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: 146.16
Avg Depth: 14.34,)
Avg Leaves: 146.71
Avg Depth: 14.62,)
````

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

````
3-element Vector{Float64}:
26.796
22.617000000000004
35.983
27.296
22.462
35.058
````

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

````
4.776490099009901
4.442277227722772
````

`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-769
Tag: RandomForestRegressor-307
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 4.78 │
│ LPLoss( │ predict │ 4.44 │
│ p = 1) │ │ │
│ RSquared() │ predict │ 0.251 │
│ RSquared() │ predict │ 0.342 │
└────────────┴───────────┴─────────────┘
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-276
Tag: RandomForestRegressor-561
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 3.07 │ 0.91 │
│ A │ LPLoss( │ predict │ 3.03 │ 0.89 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.64 │ 0.17 │
│ B │ RSquared() │ predict │ 0.65 │ 0.18 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬───────────────────────────────────────────┐
│ │ 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] │
│ A │ [2.21, 2.38, 3.28, 2.39, 4.94, 2.99] │
│ B │ [0.732, 0.832, 0.73, 0.835, 0.429, 0.366] │
└───┴───────────────────────────────────────────┘
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-837
Tag: RandomForestRegressor-822
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 2.5 │ 0.18 │
│ A │ LPLoss( │ predict │ 2.45 │ 0.12 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.814 │ 0.046 │
│ B │ RSquared() │ predict │ 0.833 │ 0.024 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬──────────────────────────────────────────────────────────────────────┐
│ │ 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] │
│ A │ [2.29, 2.73, 2.23, 2.64, 2.66, 2.16, 2.44, 2.47, 2.45, 2.44] │
│ B │ [0.878, 0.778, 0.855, 0.844, 0.787, 0.89, 0.84, 0.819, 0.837, 0.806] │
└───┴──────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand All @@ -374,8 +374,8 @@ e.uncertainty_radius_95

````
2-element Vector{Float64}:
0.18403789695002817
0.04574117004868558
0.12299683891930223
0.023901024773101298
````

# 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=>0.97, >50K=>0.03)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.26, >50K=>0.74)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.91, >50K=>0.09)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.27, >50K=>0.73)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
````

Expand All @@ -632,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.97
>50K ┤■ 0.03
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.91
>50K ┤■■■ 0.09
└ ┘
````

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

````
0.7799047960280493
0.7815427138250499
````

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

````
1.5392550897855832
1.5747266509203728
````

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-419
Tag: RandomForestClassifier-377
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
│ Accuracy() │ predict_mode │ 0.782 │
│ LogLoss( │ predict │ 1.56 │
│ Accuracy() │ predict_mode │ 0.781 │
│ LogLoss( │ predict │ 1.57 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴──────────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
Expand Down