Skip to content

Commit Notebook Markdown Changes #89

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
github-actions wants to merge 1 commit into from
Closed

Commit Notebook Markdown Changes #89

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
87 changes: 43 additions & 44 deletions docs/src/notebooks/UsingMLJ/01_basics/notebook.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,14 +14,13 @@ given [here](@ref instructions).
### Video Timings

- 00:00 Introduction
- 01:05 Goals
- 01:48 Prerequisites and Getting Help
- 02:21 Supervised Learning Recap
- 05:31 Models and Machines
- 09:28 **Live Coding:** Regression
- 28:55 Scientific Types
- 32:29 **Live Coding:** Scitypes and Classification
- 55:45 End
- 01:10 Goals
- 01:57 Prerequisites and Getting Help
- 02:33 Supervised Learning Recap
- 05:48 Models and Machines
- 09:49 **Live Coding:** Regression
- 29:20 Scientific Types
- 32:52 **Live Coding:** Scitypes and Classification

We start by inspecting the packages, and their exact versions, in the currently active
package environment:
Expand Down Expand Up @@ -199,8 +198,8 @@ fitted_params(mach)
````
(forest = Ensemble of Decision Trees
Trees: 100
Avg Leaves: 146.21
Avg Depth: 14.61,)
Avg Leaves: 146.59
Avg Depth: 14.81,)
````

````@julia
Expand All @@ -219,9 +218,9 @@ predict(mach, X)[1:3]

````
3-element Vector{Float64}:
27.011
22.410999999999994
34.27
25.982999999999997
22.639
34.983999999999995
````

Predict in the `test` rows:
Expand All @@ -237,7 +236,7 @@ mae(ypred, y[test])
````

````
4.574237623762377
4.5643613861386125
````

`mae` is actually just an alias:
Expand Down Expand Up @@ -284,14 +283,14 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-336
Tag: RandomForestRegressor-156
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 4.57 │
│ LPLoss( │ predict │ 4.56 │
│ p = 1) │ │ │
│ RSquared() │ predict │ 0.33 │
│ RSquared() │ predict │ 0.326 │
└────────────┴───────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand All @@ -313,21 +312,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-817
Tag: RandomForestRegressor-481
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 3.05 │ 0.89 │
│ A │ LPLoss( │ predict │ 2.99 │ 0.87 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.65 │ 0.16 │
│ B │ RSquared() │ predict │ 0.67 │ 0.15 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬─────────────────────────────────────────┐
│ │ per_fold │
├───┼─────────────────────────────────────────┤
│ A │ [2.27, 2.36, 3.25, 2.48, 4.97, 2.96] │
│ B │ [0.723, 0.81, 0.742, 0.806, 0.441, 0.4] │
└───┴─────────────────────────────────────────┘
┌───┬──────────────────────────────────────────┐
│ │ per_fold │
├───┼──────────────────────────────────────────┤
│ A │ [2.25, 2.34, 3.23, 2.45, 4.89, 2.81] │
│ B │ [0.72, 0.831, 0.732, 0.82, 0.434, 0.459] │
└───┴──────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

Expand All @@ -350,21 +349,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-621
Tag: RandomForestRegressor-120
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 2.48 │ 0.1 │
│ A │ LPLoss( │ predict │ 2.436 │ 0.085 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.817 │ 0.038 │
│ B │ RSquared() │ predict │ 0.84 │ 0.022 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬──────────────────────────────────────────────────────────────────────┐
│ │ per_fold │
├───┼──────────────────────────────────────────────────────────────────────┤
│ A │ [2.37, 2.74, 2.27, 2.42, 2.75, 2.54, 2.36, 2.48, 2.41, 2.5] │
│ B │ [0.87, 0.702, 0.857, 0.848, 0.824, 0.72, 0.843, 0.834, 0.839, 0.834] │
└───┴──────────────────────────────────────────────────────────────────────┘
┌───┬────────────────────────────────────────────────────────────────────────┐
│ │ per_fold │
├───┼────────────────────────────────────────────────────────────────────────┤
│ A │ [2.41, 2.46, 2.57, 2.36, 2.45, 2.39, 2.18, 2.55, 2.64, 2.36] │
│ B │ [0.848, 0.846, 0.836, 0.855, 0.819, 0.857, 0.865, 0.847, 0.757, 0.874] │
└───┴────────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

Expand All @@ -374,8 +373,8 @@ e.uncertainty_radius_95

````
2-element Vector{Float64}:
0.10349121844149421
0.03773282475420102
0.08509364727613682
0.021656217946986845
````

# Interlude on scientific types
Expand Down Expand Up @@ -620,7 +619,7 @@ 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=>0.89, >50K=>0.11)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.9, >50K=>0.1)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.28, >50K=>0.72)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
````
Expand All @@ -632,8 +631,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
>50K ┤■■■■ 0.11
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.9
>50K ┤■■■■ 0.1
└ ┘
````

Expand All @@ -644,15 +643,15 @@ accuracy(ypoint, y[test])
````

````
0.7821057480677689
0.7809796795823309
````

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

````
1.53133125245395
1.5800432929507295
````

Evaluate with one command:
Expand All @@ -672,13 +671,13 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestClassifier-321
Tag: RandomForestClassifier-844
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
│ Accuracy() │ predict_mode │ 0.782 │
│ LogLoss( │ predict │ 1.55 │
│ LogLoss( │ predict │ 1.52 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴──────────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
Expand Down
23 changes: 11 additions & 12 deletions docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,16 +14,15 @@ given [here](@ref instructions).
### Video Timings

- 00:00 Introduction
- 00:08 Goals
- 01:19 Prerequisites and Getting Help
- 02:00 Composite Models Defined
- 04:13 Model Pipelines
- 05:59 Data Leakage
- 08:37 Target Transformations
- 12:43 Live Coding: Pipelines
- 18:42 Live Coding: Target Transformations
- 20:35 Other Model Wrappers
- 25:02 End
- 00:13 Goals
- 01:28 Prerequisites and Getting Help
- 02:12 Composite Models Defined
- 04:28 Model Pipelines
- 06:07 Data Leakage
- 09:00 Target Transformations
- 13:10 **Live Coding:** Pipelines
- 19:11 **Live Coding:** Target Transformations
- 21:01 Other Model Wrappers

````@julia
using MLJ
Expand Down Expand Up @@ -160,7 +159,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-500
Tag: DeterministicPipeline-303
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down Expand Up @@ -225,7 +224,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: TransformedTargetModelDeterministic-470
Tag: TransformedTargetModelDeterministic-135
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down
Loading
Loading