From 48d332410ad5597f736d944db4525f6f16f79997 Mon Sep 17 00:00:00 2001 From: Thibaut Lienart Date: Thu, 20 Feb 2020 00:43:08 +0100 Subject: [PATCH] fd-update --- __site/assets/literate/A-fit-predict.md | 2 +- __site/assets/literate/D0-loading.md | 2 +- __site/assets/literate/ISL-lab-3.md | 2 +- __site/assets/literate/ISL-lab-5.md | 2 +- __site/css/extra.css | 2 +- __site/css/franklin.css | 2 +- __site/data/loading/index.html | 2 +- .../generated/notebooks/A-fit-predict.ipynb | 2 +- __site/generated/notebooks/D0-loading.ipynb | 2 +- __site/generated/notebooks/ISL-lab-3.ipynb | 2 +- __site/generated/notebooks/ISL-lab-5.ipynb | 2 +- __site/generated/scripts/A-fit-predict.jl | 2 +- __site/generated/scripts/D0-loading.jl | 2 +- __site/generated/scripts/ISL-lab-3.jl | 2 +- __site/generated/scripts/ISL-lab-5.jl | 2 +- .../fit-and-predict/index.html | 2 +- __site/index.html | 48 +++++++++---------- __site/isl/lab-3/index.html | 2 +- __site/isl/lab-5/index.html | 2 +- __site/libs/lunr/lunr_index.js | 4 +- _literate/A-fit-predict.jl | 2 +- _literate/D0-loading.jl | 2 +- _literate/ISL-lab-3.jl | 2 +- _literate/ISL-lab-5.jl | 2 +- index.md | 48 +++++++++---------- 25 files changed, 72 insertions(+), 72 deletions(-) diff --git a/__site/assets/literate/A-fit-predict.md b/__site/assets/literate/A-fit-predict.md index 0b99c2fe..d2b1a9c5 100644 --- a/__site/assets/literate/A-fit-predict.md +++ b/__site/assets/literate/A-fit-predict.md @@ -7,7 +7,7 @@ ### Data -As in "[choosing a model](choosing-a-model.html)", let's load the Iris dataset and unpack it: +As in "[choosing a model](/getting-started/choosing-a-model/)", let's load the Iris dataset and unpack it: ```julia:ex1 using MLJ, Statistics, PrettyPrinting diff --git a/__site/assets/literate/D0-loading.md b/__site/assets/literate/D0-loading.md index b4766614..f159a927 100644 --- a/__site/assets/literate/D0-loading.md +++ b/__site/assets/literate/D0-loading.md @@ -26,7 +26,7 @@ The `dataset` function returns a `DataFrame` object from the [DataFrames.jl](htt typeof(boston) ``` -For a short introduction to DataFrame objects, see [this tutorial](/pub/data/dataframe.html). +For a short introduction to DataFrame objects, see [this tutorial](/data/). ## Using CSV diff --git a/__site/assets/literate/ISL-lab-3.md b/__site/assets/literate/ISL-lab-3.md index a241d136..0cd86348 100644 --- a/__site/assets/literate/ISL-lab-3.md +++ b/__site/assets/literate/ISL-lab-3.md @@ -32,7 +32,7 @@ describe(boston, :mean, :std, :eltype) ``` So there's no missing value and most variables are encoded as floating point numbers. -In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/pub/getting-started/choosing-a-model.html#data_and_its_interpretation) on scientific types. +In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/getting-started/choosing-a-model/#data_and_its_interpretation) on scientific types. Here we will just interpret the integer features as continuous as we will just use a basic linear regression: diff --git a/__site/assets/literate/ISL-lab-5.md b/__site/assets/literate/ISL-lab-5.md index 90bb874f..77dc4ae2 100644 --- a/__site/assets/literate/ISL-lab-5.md +++ b/__site/assets/literate/ISL-lab-5.md @@ -136,7 +136,7 @@ savefig(joinpath(@OUTPUT, "ISL-lab-5-g3.svg")) # hide Let's crossvalidate over the degree of the polynomial. -**Note**: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/pub/getting-started/model-tuning.html) for a gentler introduction to model tuning. +**Note**: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/getting-started/model-tuning/) for a gentler introduction to model tuning. The gist of the following code is to create a dataframe where each column is a power of the `Horsepower` feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree): ```julia:ex11 diff --git a/__site/css/extra.css b/__site/css/extra.css index 989980e3..be7e4eab 100644 --- a/__site/css/extra.css +++ b/__site/css/extra.css @@ -1 +1 @@ -@charset "utf-8"; .franklin-content{margin-top:5em;padding-left:10%}@media (min-width:1050px){.franklin-content{width:700px;margin-left:0;padding-left:80px}.header{width:760px}}#menu-logo-link{text-decoration:none}.menu-logo img{width:175px;padding:10px;padding-top:40px;padding-left:20px}.menu-logo{font-family:'Lato',Arial;font-size:25px;text-decoration:none;color:#222;text-rendering:optimizeLegibility;padding:0;margin:0;margin-top:-20px;text-align:center;padding-bottom:10px;margin-bottom:10px}ul.pure-menu-list{margin-top:15px;border-top:1px solid black}ul.pure-menu-sublist{box-shadow:inset 0 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Loading and elementary processing of data

Download the notebook, the raw script, or the annotated script for this tutorial (right-click on the link and save).
  1. Using RDatasets
  2. Using CSV
    1. Basic usage
    2. Example 1
    3. Example 2
In this short tutorial we discuss two ways to easily load data in Julia:

  1. loading a standard dataset via RDatasets.jl,

  2. loading a local file with CSV.jl,

Using RDatasets

The package RDatasets.jl provides access to most of the many datasets listed on this page. These are well known, standard datasets that can be used to get started with data processing and classical machine learning such as for instance iris, crabs, Boston, etc.

To load such a dataset, you will need to specify which R package it belongs to as well as its name; for instance Boston is part of MASS.

using RDatasets
 
 boston = dataset("MASS", "Boston");

The fact that Boston is part of MASS is clearly indicated on the list linked to earlier. While it can be a bit slow, loading a dataset via RDatasets is very simple and convenient as you don't have to worry about setting the names of columns etc.

The dataset function returns a DataFrame object from the DataFrames.jl package.

typeof(boston)
DataFrames.DataFrame
-

For a short introduction to DataFrame objects, see this tutorial.

+

For a short introduction to DataFrame objects, see this tutorial.

Using CSV

The package CSV.jl offers a powerful way to read arbitrary CSV files efficiently. In particular the CSV.read function allows to read a file and return a DataFrame.

Basic usage

diff --git a/__site/generated/notebooks/A-fit-predict.ipynb b/__site/generated/notebooks/A-fit-predict.ipynb index a9e7a779..baec7ff5 100644 --- a/__site/generated/notebooks/A-fit-predict.ipynb +++ b/__site/generated/notebooks/A-fit-predict.ipynb @@ -27,7 +27,7 @@ "\n", "### Data\n", "\n", - "As in \"[choosing a model](choosing-a-model.html)\", let's load the Iris dataset and unpack it:" + "As in \"[choosing a model](/getting-started/choosing-a-model/)\", let's load the Iris dataset and unpack it:" ], "metadata": {} }, diff --git a/__site/generated/notebooks/D0-loading.ipynb b/__site/generated/notebooks/D0-loading.ipynb index 498c645a..7e9f0a35 100644 --- a/__site/generated/notebooks/D0-loading.ipynb +++ b/__site/generated/notebooks/D0-loading.ipynb @@ -68,7 +68,7 @@ "outputs": [], "cell_type": "markdown", "source": [ - "For a short introduction to DataFrame objects, see [this tutorial](/pub/data/dataframe.html)." + "For a short introduction to DataFrame objects, see [this tutorial](/data/)." ], "metadata": {} }, diff --git a/__site/generated/notebooks/ISL-lab-3.ipynb b/__site/generated/notebooks/ISL-lab-3.ipynb index 63ab8ab9..49fd0f14 100644 --- a/__site/generated/notebooks/ISL-lab-3.ipynb +++ b/__site/generated/notebooks/ISL-lab-3.ipynb @@ -85,7 +85,7 @@ "cell_type": "markdown", "source": [ "So there's no missing value and most variables are encoded as floating point numbers.\n", - "In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/pub/getting-started/choosing-a-model.html#data_and_its_interpretation) on scientific types.\n", + "In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/getting-started/choosing-a-model/#data_and_its_interpretation) on scientific types.\n", "\n", "Here we will just interpret the integer features as continuous as we will just use a basic linear regression:" ], diff --git a/__site/generated/notebooks/ISL-lab-5.ipynb b/__site/generated/notebooks/ISL-lab-5.ipynb index 4d97145f..7d1bf7d7 100644 --- a/__site/generated/notebooks/ISL-lab-5.ipynb +++ b/__site/generated/notebooks/ISL-lab-5.ipynb @@ -290,7 +290,7 @@ "\n", "Let's crossvalidate over the degree of the polynomial.\n", "\n", - "**Note**: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/pub/getting-started/model-tuning.html) for a gentler introduction to model tuning.\n", + "**Note**: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/getting-started/model-tuning/) for a gentler introduction to model tuning.\n", "The gist of the following code is to create a dataframe where each column is a power of the `Horsepower` feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree):" ], "metadata": {} diff --git a/__site/generated/scripts/A-fit-predict.jl b/__site/generated/scripts/A-fit-predict.jl index 2cd39b79..185e826a 100644 --- a/__site/generated/scripts/A-fit-predict.jl +++ b/__site/generated/scripts/A-fit-predict.jl @@ -7,7 +7,7 @@ # using Pkg; Pkg.activate("."); Pkg.instantiate() # ``` -# [MLJ.jl]: https://github.com/alan-turing-institute/MLJ.jl# [RDatasets.jl]: https://github.com/JuliaStats/RDatasets.jl# [DecisionTree.jl]: https://github.com/bensadeghi/DecisionTree.jl## ## Preliminary steps## ### Data## As in "[choosing a model](choosing-a-model.html)", let's load the Iris dataset and unpack it: +# [MLJ.jl]: https://github.com/alan-turing-institute/MLJ.jl# [RDatasets.jl]: https://github.com/JuliaStats/RDatasets.jl# [DecisionTree.jl]: https://github.com/bensadeghi/DecisionTree.jl## ## Preliminary steps## ### Data## As in "[choosing a model](/getting-started/choosing-a-model/)", let's load the Iris dataset and unpack it: using MLJ, Statistics, PrettyPrinting X, y = @load_iris; diff --git a/__site/generated/scripts/D0-loading.jl b/__site/generated/scripts/D0-loading.jl index 8bcc656d..afccc0b0 100644 --- a/__site/generated/scripts/D0-loading.jl +++ b/__site/generated/scripts/D0-loading.jl @@ -15,7 +15,7 @@ boston = dataset("MASS", "Boston"); # The fact that `Boston` is part of `MASS` is clearly indicated on the [list](http://vincentarelbundock.github.io/Rdatasets/datasets.html) linked to earlier.# While it can be a bit slow, loading a dataset via RDatasets is very simple and convenient as you don't have to worry about setting the names of columns etc.## The `dataset` function returns a `DataFrame` object from the [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) package. typeof(boston) -# For a short introduction to DataFrame objects, see [this tutorial](/pub/data/dataframe.html). +# For a short introduction to DataFrame objects, see [this tutorial](/data/). # ## Using CSV## The package [CSV.jl](https://github.com/JuliaData/CSV.jl) offers a powerful way to read arbitrary CSV files efficiently.# In particular the `CSV.read` function allows to read a file and return a DataFrame.## ### Basic usage## Let's say you have a file `foo.csv` at some path `fpath=joinpath("data", "foo.csv")` with the content## ```# col1,col2,col3,col4,col5,col6,col7,col8# ,1,1.0,1,one,2019-01-01,2019-01-01T00:00:00,true# ,2,2.0,2,two,2019-01-02,2019-01-02T00:00:00,false# ,3,3.0,3.14,three,2019-01-03,2019-01-03T00:00:00,true# ``` c = """ diff --git a/__site/generated/scripts/ISL-lab-3.jl b/__site/generated/scripts/ISL-lab-3.jl index 3497949c..f4d1f741 100644 --- a/__site/generated/scripts/ISL-lab-3.jl +++ b/__site/generated/scripts/ISL-lab-3.jl @@ -21,7 +21,7 @@ first(boston, 3) # Let's get a feel for the data describe(boston, :mean, :std, :eltype) -# So there's no missing value and most variables are encoded as floating point numbers.# In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/pub/getting-started/choosing-a-model.html#data_and_its_interpretation) on scientific types.## Here we will just interpret the integer features as continuous as we will just use a basic linear regression: +# So there's no missing value and most variables are encoded as floating point numbers.# In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/getting-started/choosing-a-model/#data_and_its_interpretation) on scientific types.## Here we will just interpret the integer features as continuous as we will just use a basic linear regression: data = coerce(boston, autotype(boston, :discrete_to_continuous)); # Let's also extract the target variable (`MedV`): diff --git a/__site/generated/scripts/ISL-lab-5.jl b/__site/generated/scripts/ISL-lab-5.jl index 6e3e9850..7e330480 100644 --- a/__site/generated/scripts/ISL-lab-5.jl +++ b/__site/generated/scripts/ISL-lab-5.jl @@ -106,7 +106,7 @@ ylabel("MPG", fontsize=14) # \figalt{1st, 2nd and 3d order fit}{ISL-lab-5-g3.svg} -# ## K-Folds Cross Validation## Let's crossvalidate over the degree of the polynomial.## **Note**: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/pub/getting-started/model-tuning.html) for a gentler introduction to model tuning.# The gist of the following code is to create a dataframe where each column is a power of the `Horsepower` feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree): +# ## K-Folds Cross Validation## Let's crossvalidate over the degree of the polynomial.## **Note**: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/getting-started/model-tuning/) for a gentler introduction to model tuning.# The gist of the following code is to create a dataframe where each column is a power of the `Horsepower` feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree): Xhp = DataFrame([hp.^i for i in 1:10]) cases = [[Symbol("x$j") for j in 1:i] for i in 1:10] diff --git a/__site/getting-started/fit-and-predict/index.html b/__site/getting-started/fit-and-predict/index.html index da9a368b..a7e40805 100644 --- a/__site/getting-started/fit-and-predict/index.html +++ b/__site/getting-started/fit-and-predict/index.html @@ -1,4 +1,4 @@ - Fit, predict, transform

Fit, predict, transform

Download the notebook, the raw script, or the annotated script for this tutorial (right-click on the link and save).
  1. Preliminary steps
    1. Data
    2. MLJ Machine
  2. Training and testing a supervised model
    1. Splitting the data
    2. Fitting and testing the machine
  3. Unsupervised models

Preliminary steps

Data

As in "choosing a model", let's load the Iris dataset and unpack it:

using MLJ, Statistics, PrettyPrinting
+         Fit, predict, transform    
   
 
 
Fit, predict, transform
 Download the notebook, the raw script, or the annotated script for this tutorial (right-click on the link and save). 
  1. Preliminary steps
    1. Data
    2. MLJ Machine
  2. Training and testing a supervised model
    1. Splitting the data
    2. Fitting and testing the machine
  3. Unsupervised models
 
Preliminary steps
 
Data
 
As in "choosing a model", let's load the Iris dataset and unpack it:
 
using MLJ, Statistics, PrettyPrinting
 X, y = @load_iris;
 
let's also load the DecisionTreeClassifier:
 
@load DecisionTreeClassifier
 tree_model = DecisionTreeClassifier()
DecisionTreeClassifier(
     max_depth = -1,
diff --git a/__site/index.html b/__site/index.html
index 3b87d94d..1878a68b 100644
--- a/__site/index.html
+++ b/__site/index.html
@@ -3,33 +3,33 @@ 
DataFrames, CSV and CategoricalArrays.

 
 
 
Getting started with MLJ

 
If you are new to MLJ but are familiar with Julia and with Machine Learning, we recommend you start by going through the short Getting started examples in order:

 
    
-
  1. How to choose a model,
    
+
  2. How to choose a model,
    
 
-
  3. How to fit, predict and transform
    
+
  4. How to fit, predict and transform
    
 
-
  5. How to tune models
    
+
  6. How to tune models
    
 
-
  7. How to ensemble models
    
+
  8. How to ensemble models
    
 
-
  9. How to ensemble models (2)
    
+
  10. How to ensemble models (2)
    
 
-
  11. How to compose models
    
+
  12. How to compose models
    
 
-
  13. How to build a learning network
    
+
  14. How to build a learning network
    
 
-
  15. How to create models from learning networks
    
+
  16. How to create models from learning networks
    
 
 

 
Additionally, you can refer to the documentation for more detailed information.

@@ -37,21 +37,21 @@ 
An introduction to statistical learning which were originally written in R. These tutorials may be useful if you want a gentle intro to MLJ and other relevant tools in the Julia environment. If you're fairly new to Julia and ML, this is probably where you should start.

 
Note: the adaptation is fairly liberal, adding content when it helps highlights specificities with MLJ and removing content when it seems unnecessary. Also note that some of the things used in the ISL labs are not (yet) supported by MLJ.

 
    
-
  • Lab 2, a very short intro to Julia for data analysis
    
+
  • Lab 2, a very short intro to Julia for data analysis
    
 
-
  • Lab 3, linear regression and metrics
    
+
  • Lab 3, linear regression and metrics
    
 
-
  • Lab 4, classification with LDA, QDA, KNN and metrics
    
+
  • Lab 4, classification with LDA, QDA, KNN and metrics
    
 
-
  • Lab 5, k-folds cross validation
    
+
  • Lab 5, k-folds cross validation
    
 
-
  • Lab 6b, Ridge and Lasso regression
    
+
  • Lab 6b, Ridge and Lasso regression
    
 
-
  • Lab 8, Tree-based models
    
+
  • Lab 8, Tree-based models
    
 
-
  • Lab 9, SVM (partial)
    
+
  • Lab 9, SVM (partial)
    
 
-
  • Lab 10, PCA and clustering (partial)
    
+
  • Lab 10, PCA and clustering (partial)
    
 
 

 
End to end examples with MLJ

@@ -59,13 +59,13 @@ 
AMES, simple, regression, one-hot, learning network, tuning, deterministic

+
  • AMES, simple, regression, one-hot, learning network, tuning, deterministic
    
 
-
  • Wine, simple, classification, standardizer, PCA, knn, multinomial, pipeline
    
+
  • Wine, simple, classification, standardizer, PCA, knn, multinomial, pipeline
    
 
-
  • Crabs XGB, simple, classification, xg-boost, tuning
    
+
  • Crabs XGB, simple, classification, xg-boost, tuning
    
 
-
  • Horse, simple, classification, scientific type and autotype, missing values, imputation, one-hot, tuning
    
+
  • Horse, simple, classification, scientific type and autotype, missing values, imputation, one-hot, tuning
    
 
 
 
diff --git a/__site/isl/lab-3/index.html b/__site/isl/lab-3/index.html
index 5455da91..5c3bcd29 100644
--- a/__site/isl/lab-3/index.html
+++ b/__site/isl/lab-3/index.html
@@ -27,7 +27,7 @@
 │ 11  │ PTRatio  │ 18.4555  │ 2.16495  │ Float64  │
 │ 12  │ Black    │ 356.674  │ 91.2949  │ Float64  │
 │ 13  │ LStat    │ 12.6531  │ 7.14106  │ Float64  │
-│ 14  │ MedV     │ 22.5328  │ 9.1971   │ Float64  │
    •  
    So there's no missing value and most variables are encoded as floating point numbers. In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also this tutorial section on scientific types.
     
    Here we will just interpret the integer features as continuous as we will just use a basic linear regression:
     
    data = coerce(boston, autotype(boston, :discrete_to_continuous));
    
+│ 14  │ MedV     │ 22.5328  │ 9.1971   │ Float64  │

    So there's no missing value and most variables are encoded as floating point numbers. In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also this tutorial section on scientific types.

    Here we will just interpret the integer features as continuous as we will just use a basic linear regression:

    data = coerce(boston, autotype(boston, :discrete_to_continuous));

    Let's also extract the target variable (MedV):

    y = data.MedV
 X = select(data, Not(:MedV));
    diff --git a/__site/isl/lab-5/index.html b/__site/isl/lab-5/index.html index 341e1a7e..3f6482f4 100644 --- a/__site/isl/lab-5/index.html +++ b/__site/isl/lab-5/index.html @@ -79,7 +79,7 @@ 1st, 2nd and 3d order fit

    K-Folds Cross Validation

    Let's crossvalidate over the degree of the polynomial.

    -

    Note: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on tuning models for a gentler introduction to model tuning. The gist of the following code is to create a dataframe where each column is a power of the Horsepower feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree):

    +

    Note: there's a bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on tuning models for a gentler introduction to model tuning. The gist of the following code is to create a dataframe where each column is a power of the Horsepower feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree):

    Xhp = DataFrame([hp.^i for i in 1:10])
 
 cases = [[Symbol("x$j") for j in 1:i] for i in 1:10]
diff --git a/__site/libs/lunr/lunr_index.js b/__site/libs/lunr/lunr_index.js
index 24af9019..208acd80 100644
--- a/__site/libs/lunr/lunr_index.js
+++ b/__site/libs/lunr/lunr_index.js
@@ -1,2 +1,2 @@
-const LUNR_DATA = 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\ No newline at end of file
+const LUNR_DATA = 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+const PREVIEW_LOOKUP = {"0":{"t":"Handling categorical data","l":"/MLJTutorials/data/categorical/index.html"},"1":{"t":"Manipulating a DataFrame","l":"/MLJTutorials/data/dataframe/index.html"},"2":{"t":"Loading and elementary processing of data","l":"/MLJTutorials/data/loading/index.html"},"3":{"t":"Data interpretation: Scientific Types","l":"/MLJTutorials/data/scitype/index.html"},"4":{"t":"AMES","l":"/MLJTutorials/end-to-end/AMES/index.html"},"5":{"t":"Crabs with XGBoost","l":"/MLJTutorials/end-to-end/crabs-xgb/index.html"},"6":{"t":"Horse colic data","l":"/MLJTutorials/end-to-end/horse/index.html"},"7":{"t":"Wine","l":"/MLJTutorials/end-to-end/wine/index.html"},"8":{"t":"Choosing and evaluating a model","l":"/MLJTutorials/getting-started/choosing-a-model/index.html"},"9":{"t":"Composing models","l":"/MLJTutorials/getting-started/composing-models/index.html"},"10":{"t":"Ensemble models","l":"/MLJTutorials/getting-started/ensembles/index.html"},"11":{"t":"Ensemble models (2)","l":"/MLJTutorials/getting-started/ensembles-2/index.html"},"12":{"t":"Fit, predict, transform","l":"/MLJTutorials/getting-started/fit-and-predict/index.html"},"13":{"t":"Learning networks","l":"/MLJTutorials/getting-started/learning-networks/index.html"},"14":{"t":"Learning networks 2","l":"/MLJTutorials/getting-started/learning-networks-2/index.html"},"15":{"t":"Tuning a model","l":"/MLJTutorials/getting-started/model-tuning/index.html"},"16":{"t":"MLJ Tutorials","l":"/MLJTutorials/index.html"},"17":{"t":"Lab 10 - PCA and Clustering","l":"/MLJTutorials/isl/lab-10/index.html"},"18":{"t":"Lab 2","l":"/MLJTutorials/isl/lab-2/index.html"},"19":{"t":"Lab 3 - Linear Regression","l":"/MLJTutorials/isl/lab-3/index.html"},"20":{"t":"Lab 4 - Logistic Regression, LDA, QDA, KNN","l":"/MLJTutorials/isl/lab-4/index.html"},"21":{"t":"Lab 5 - Cross validation and the bootstrap","l":"/MLJTutorials/isl/lab-5/index.html"},"22":{"t":"Lab 6b - Ridge and Lasso regression","l":"/MLJTutorials/isl/lab-6b/index.html"},"23":{"t":"Lab 8 - Tree-based models","l":"/MLJTutorials/isl/lab-8/index.html"},"24":{"t":"Lab 9 - Support Vector Machine","l":"/MLJTutorials/isl/lab-9/index.html"},"25":{"t":"Search ⋅ MLJTutorials","l":"/MLJTutorials/search/index.html"}};
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diff --git a/_literate/A-fit-predict.jl b/_literate/A-fit-predict.jl
index 9264c658..1a5f65f9 100644
--- a/_literate/A-fit-predict.jl
+++ b/_literate/A-fit-predict.jl
@@ -7,7 +7,7 @@
 #
 # ### Data
 #
-# As in "[choosing a model](choosing-a-model.html)", let's load the Iris dataset and unpack it:
+# As in "[choosing a model](/getting-started/choosing-a-model/)", let's load the Iris dataset and unpack it:
 
 using MLJ, Statistics, PrettyPrinting
 MLJ.color_off() # hide
diff --git a/_literate/D0-loading.jl b/_literate/D0-loading.jl
index 5aaeb919..de7abdf2 100644
--- a/_literate/D0-loading.jl
+++ b/_literate/D0-loading.jl
@@ -21,7 +21,7 @@ boston = dataset("MASS", "Boston");
 
 typeof(boston)
 
-# For a short introduction to DataFrame objects, see [this tutorial](/pub/data/dataframe.html).
+# For a short introduction to DataFrame objects, see [this tutorial](/data/).
 
 # ## Using CSV
 #
diff --git a/_literate/ISL-lab-3.jl b/_literate/ISL-lab-3.jl
index 4994169d..f7f76468 100644
--- a/_literate/ISL-lab-3.jl
+++ b/_literate/ISL-lab-3.jl
@@ -25,7 +25,7 @@ first(boston, 3)
 describe(boston, :mean, :std, :eltype)
 
 # So there's no missing value and most variables are encoded as floating point numbers.
-# In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/pub/getting-started/choosing-a-model.html#data_and_its_interpretation) on scientific types.
+# In MLJ it's important to specify the interpretation of the features (should it be considered as a Continuous feature, as a Count, ...?), see also [this tutorial section](/getting-started/choosing-a-model/#data_and_its_interpretation) on scientific types.
 #
 # Here we will just interpret the integer features as continuous as we will just use a basic linear regression:
 
diff --git a/_literate/ISL-lab-5.jl b/_literate/ISL-lab-5.jl
index 9fa3f46b..7e025bf4 100644
--- a/_literate/ISL-lab-5.jl
+++ b/_literate/ISL-lab-5.jl
@@ -116,7 +116,7 @@ savefig(joinpath(@OUTPUT, "ISL-lab-5-g3.svg")) # hide
 #
 # Let's crossvalidate over the degree of the  polynomial.
 #
-# **Note**: there's a  bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/pub/getting-started/model-tuning.html) for a gentler introduction to model tuning.
+# **Note**: there's a  bit of gymnastics here because MLJ doesn't directly support a polynomial regression; see our tutorial on [tuning models](/getting-started/model-tuning/) for a gentler introduction to model tuning.
 # The gist of the following code is to create a dataframe where each column is a power of the `Horsepower` feature from 1 to 10 and we build a series of regression models using incrementally more of those features (higher degree):
 
 Xhp = DataFrame([hp.^i for i in 1:10])
diff --git a/index.md b/index.md
index f213f168..342349da 100644
--- a/index.md
+++ b/index.md
@@ -23,23 +23,23 @@ In these we provide an introduction to some of the fundamental packages in the J
 [CSV]: https://github.com/JuliaData/CSV.jl
 [CategoricalArrays]: https://github.com/JuliaData/CategoricalArrays.jl
 
-* How to [load data](/pub/data/loading.html),
-* Short intro to [dataframes](/pub/data/loading.html),
-* Dealing with [categorical data](/pub/data/categorical.html)
-* Specifying [data interpretation](/pub/data/scitype.html)
+* How to [load data](/data/loading/),
+* Short intro to [dataframes](/data/dataframe/),
+* Dealing with [categorical data](/data/categorical/)
+* Specifying [data interpretation](/data/scitype/)
 
 ## Getting started with MLJ
 
 If you are new to MLJ but are familiar with Julia and with Machine Learning, we recommend you start by going through the short _Getting started_ examples in order:
 
-1. How to [choose a model](/pub/getting-started/choosing-a-model.html),
-1. How to [fit, predict and transform](/pub/getting-started/fit-and-predict.html)
-1. How to [tune models](/pub/getting-started/model-tuning.html)
-1. How to [ensemble models](/pub/getting-started/ensembles.html)
-1. How to [ensemble models (2)](/pub/getting-started/ensembles-2.html)
-1. How to [compose models](/pub/getting-started/composing-models.html)
-1. How to build a [learning network](/pub/getting-started/learning-networks.html)
-1. How to [create models](/pub/getting-started/learning-networks-2.html) from learning networks
+1. How to [choose a model](/getting-started/choosing-a-model/),
+1. How to [fit, predict and transform](/getting-started/fit-and-predict/)
+1. How to [tune models](/getting-started/model-tuning/)
+1. How to [ensemble models](/getting-started/ensembles/)
+1. How to [ensemble models (2)](/getting-started/ensembles-2/)
+1. How to [compose models](/getting-started/composing-models/)
+1. How to build a [learning network](/getting-started/learning-networks/)
+1. How to [create models](/getting-started/learning-networks-2/) from learning networks
 
 Additionally, you can refer to the [documentation](https://alan-turing-institute.github.io/MLJ.jl/stable/) for more detailed information.
 
@@ -52,14 +52,14 @@ If you're fairly new to Julia and ML, this is probably where you should start.
 **Note**: the adaptation is fairly liberal, adding content when it helps highlights specificities with MLJ and removing content when it seems unnecessary.
 Also note that some of the things used in the ISL labs are not (yet) supported by MLJ.
 
-* [Lab 2](/pub/isl/lab-2.html), a very short intro to Julia for data analysis
-* [Lab 3](/pub/isl/lab-3.html), linear regression and metrics
-* [Lab 4](/pub/isl/lab-4.html), classification with LDA, QDA, KNN and metrics
-* [Lab 5](/pub/isl/lab-5.html), k-folds cross validation
-* [Lab 6b](/pub/isl/lab-6b.html), Ridge and Lasso regression
-* [Lab 8](/pub/isl/lab-8.html), Tree-based models
-* [Lab 9](/pub/isl/lab-9.html), SVM (_partial_)
-* [Lab 10](/pub/isl/lab-10.html), PCA and clustering (_partial_)
+* [Lab 2](/isl/lab-2/), a very short intro to Julia for data analysis
+* [Lab 3](/isl/lab-3/), linear regression and metrics
+* [Lab 4](/isl/lab-4/), classification with LDA, QDA, KNN and metrics
+* [Lab 5](/isl/lab-5/), k-folds cross validation
+* [Lab 6b](/isl/lab-6b/), Ridge and Lasso regression
+* [Lab 8](/isl/lab-8/), Tree-based models
+* [Lab 9](/isl/lab-9/), SVM (_partial_)
+* [Lab 10](/isl/lab-10/), PCA and clustering (_partial_)
 
 ## End to end examples with MLJ
 
@@ -70,7 +70,7 @@ Note that these tutorials are not meant to teach you ML or Data Science; there m
 
 The examples can be followed in any order, the tags can guide you as to which tutorials you may want to look at first.
 
-* [AMES](/pub/end-to-end/AMES.html), *simple*, *regression*, *one-hot*, *learning network*, *tuning*, *deterministic*
-* [Wine](/pub/end-to-end/wine.html), *simple*, *classification*, *standardizer*, *PCA*, *knn*, *multinomial*, *pipeline*
-* [Crabs XGB](/pub/end-to-end/crabs-xgb.html), *simple*, *classification*, *xg-boost*, *tuning*
-* [Horse](/pub/end-to-end/horse.html), *simple*, *classification*, *scientific type* and *autotype*, *missing values*, *imputation*, *one-hot*, *tuning*
+* [AMES](/end-to-end/AMES/), *simple*, *regression*, *one-hot*, *learning network*, *tuning*, *deterministic*
+* [Wine](/end-to-end/wine/), *simple*, *classification*, *standardizer*, *PCA*, *knn*, *multinomial*, *pipeline*
+* [Crabs XGB](/end-to-end/crabs-xgb/), *simple*, *classification*, *xg-boost*, *tuning*
+* [Horse](/end-to-end/horse/), *simple*, *classification*, *scientific type* and *autotype*, *missing values*, *imputation*, *one-hot*, *tuning*