From 582243503938e99bec7ecd7bedc4fe417665faa0 Mon Sep 17 00:00:00 2001 From: ablaom <30517088+ablaom@users.noreply.github.com> Date: Sat, 17 Oct 2026 07:24:13 +0000 Subject: [PATCH] update notebook markdown --- .../notebooks/UsingMLJ/01_basics/notebook.md | 76 +++++++++---------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md index 389d071..7aa78f7 100644 --- a/docs/src/notebooks/UsingMLJ/01_basics/notebook.md +++ b/docs/src/notebooks/UsingMLJ/01_basics/notebook.md @@ -199,8 +199,8 @@ fitted_params(mach) ```` (forest = Ensemble of Decision Trees Trees: 100 -Avg Leaves: 146.16 -Avg Depth: 14.34,) +Avg Leaves: 146.66 +Avg Depth: 14.83,) ```` ````@julia @@ -219,9 +219,9 @@ predict(mach, X)[1:3] ```` 3-element Vector{Float64}: - 26.796 - 22.617000000000004 - 35.983 + 26.535999999999998 + 22.762999999999998 + 34.841 ```` Predict in the `test` rows: @@ -237,7 +237,7 @@ mae(ypred, y[test]) ```` ```` -4.776490099009901 +4.457232673267327 ```` `mae` is actually just an alias: @@ -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-797 Extract: ┌────────────┬───────────┬─────────────┐ │ measure │ operation │ measurement │ ├────────────┼───────────┼─────────────┤ -│ LPLoss( │ predict │ 4.78 │ +│ LPLoss( │ predict │ 4.46 │ │ p = 1) │ │ │ -│ RSquared() │ predict │ 0.251 │ +│ RSquared() │ predict │ 0.344 │ └────────────┴───────────┴─────────────┘ Apply `describe` to this result for a named tuple summary. ```` @@ -313,21 +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-276 +Tag: RandomForestRegressor-427 Extract: ┌───┬────────────┬───────────┬─────────────┬─────────┐ │ │ measure │ operation │ measurement │ 1.96*SE │ ├───┼────────────┼───────────┼─────────────┼─────────┤ -│ A │ LPLoss( │ predict │ 3.07 │ 0.91 │ +│ A │ LPLoss( │ predict │ 2.99 │ 0.87 │ │ │ p = 1) │ │ │ │ -│ B │ RSquared() │ predict │ 0.64 │ 0.17 │ +│ B │ RSquared() │ predict │ 0.66 │ 0.16 │ └───┴────────────┴───────────┴─────────────┴─────────┘ -┌───┬───────────────────────────────────────────┐ -│ │ 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] │ -└───┴───────────────────────────────────────────┘ +┌───┬────────────────────────────────────────────┐ +│ │ 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] │ +└───┴────────────────────────────────────────────┘ Apply `describe` to this result for a named tuple summary. ```` @@ -350,21 +350,21 @@ 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-298 Extract: ┌───┬────────────┬───────────┬─────────────┬─────────┐ │ │ measure │ operation │ measurement │ 1.96*SE │ ├───┼────────────┼───────────┼─────────────┼─────────┤ -│ A │ LPLoss( │ predict │ 2.5 │ 0.18 │ +│ A │ LPLoss( │ predict │ 2.46 │ 0.11 │ │ │ p = 1) │ │ │ │ -│ B │ RSquared() │ predict │ 0.814 │ 0.046 │ +│ B │ RSquared() │ predict │ 0.821 │ 0.027 │ └───┴────────────┴───────────┴─────────────┴─────────┘ -┌───┬──────────────────────────────────────────────────────────────────────┐ -│ │ 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] │ -└───┴──────────────────────────────────────────────────────────────────────┘ +┌───┬────────────────────────────────────────────────────────────────────────┐ +│ │ 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] │ +└───┴────────────────────────────────────────────────────────────────────────┘ Apply `describe` to this result for a named tuple summary. ```` @@ -374,8 +374,8 @@ e.uncertainty_radius_95 ```` 2-element Vector{Float64}: - 0.18403789695002817 - 0.04574117004868558 + 0.11330666259443022 + 0.02705219873485344 ```` # Interlude on scientific types @@ -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=>0.9, >50K=>0.1) + UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.29, >50K=>0.71) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0) ```` @@ -632,8 +632,8 @@ yprob[3] ```` UnivariateFinite{ScientificTypesBase.Multiclass{2}} ┌ ┐ - <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.97 - >50K ┤■ 0.03 + <=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.9 + >50K ┤■■■■ 0.1 └ ┘ ```` @@ -644,7 +644,7 @@ accuracy(ypoint, y[test]) ```` ```` -0.7799047960280493 +0.7819521932743001 ```` ````@julia @@ -652,7 +652,7 @@ log_loss(yprob, y[test]) ```` ```` -1.5392550897855832 +1.5469870467696316 ```` Evaluate with one command: @@ -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-165 Extract: ┌──────────────────────┬──────────────┬─────────────┐ │ measure │ operation │ measurement │ ├──────────────────────┼──────────────┼─────────────┤ -│ Accuracy() │ predict_mode │ 0.782 │ -│ LogLoss( │ predict │ 1.56 │ +│ Accuracy() │ predict_mode │ 0.78 │ +│ LogLoss( │ predict │ 1.54 │ │ tol = 2.22045e-16) │ │ │ └──────────────────────┴──────────────┴─────────────┘ Apply `describe` to this result for a named tuple summary.