mlr3gallery

Comparison of Decision Boundaries of Classification Learners

classification
visualization
mlr3viz

Visuzalizes the decision boundaries of multiple classification learners on some artificial data sets.

A production example using plumber and Docker

mlr3pipelines
production

We write a REST API using plumber and deploy it using Docker.

Bike Sharing Demand - Use Case

tuning
benchmarking
nested resampling
filter
branching

This use case provides an example on tuning and benchmarking in mlr3verse using data from Capital Bikeshare.

Target transformations via pipelines

mlr3pipelines
target transformation

We show how to do various kinds of target transformations using pipelines.

Why R? Webinar - Pipelines and AutoML with mlr3

video
mlr3
mlr3pipelines

In-depth presentation of the mlr3 and mlr3pipelines packages for the Why R? 2020 Webinar Series.

mlr3 and OpenML - Moneyball use case

imputation
regression
feature importance

This use case shows how to make use of OpenML data and how to impute missing values in a ML problem.

Feature Engineering of Date-Time Variables

date features
feature engineering
mlr3pipelines

We show how to engineer features using date-time variables.

A pipeline for the titanic data set - Advanced

imputation
classification
mlr3pipelines
feature engineering

This post shows how to build a Graph using the mlr3pipelines package on the "titanic" dataset. Moreover, feature engineering, data imputation and benchmarking are covered.

Tuning a stacked learner

mlr3pipelines
mlr3tuning
stacking

This tutorial explains how to create and tune a multilevel stacking model using the mlr3pipelines package.

Pipelines, selectors, branches

mlr3pipelines

This tutorial explains how applying different preprocessing steps on different features and branching of preprocessing steps can be achieved using the mlr3pipelines package.

Imbalanced data handling with mlr3

classification
imbalanced data
tuning

This use case compares different approaches to handle class imbalance for the optdigits (https://www.openml.org/d/980) binary classification data set using the mlr3 package.

Resampling: stratified, blocked and predefined

resampling
stratification

When evaluating machine learning algorithms through resampling, it is preferable that each train/test partition will be a representative subset of the whole data set. This post covers three ways to achieve such reliable resampling procedures.

mlr3 basics on "iris" - Hello World!

mlr3
basics

Basic ML operations on iris: Train, predict, score, resample and benchmark. A simple, hands-on intro to mlr3.

A pipeline for the titanic data set - Basics

imputation
classification
mlr3pipelines
feature engineering

This post shows how to build a Graph using the mlr3pipelines package on the "titanic" dataset.

mlr3 Basics - German Credit

visualization
classification
feature importance
german credit

In this use case, we teach the basics of mlr3 by training different models on the German credit dataset.

mlr3pipelines Tutorial - German Credit

mlr3pipelines
imputation
filtering
stacking
german credit

In this use case, we continue working with the German credit dataset. We already used different Learners on it in previous posts and tried to optimize their hyperparameters. To make things interesting, we artificially introduce missing values into the dataset, perform imputation and filtering and stack Learners.

mlr3tuning Tutorial - German Credit

mlr3tuning
tuning
german credit

In this use case, we continue working with the German credit dataset. We work on hyperparameter tuning and apply nested resampling.

Select uncorrelated features

tuning
mlr3pipelines
filtering

The following example describes a situation where we aim to remove correlated features. This in essence means, that we drop features until no features have a correlation higher then a given `cutoff`. This is often useful when we for example want to use linear models.

Tuning Over Multiple Learners

tuning

This use case shows how to tune over multiple learners for a single task.

Impute missing variables

classification
imputation
mlr3pipelines

We show how to use mlr3pipelines to augment the "mlr_learners_classif.ranger" learner with automatic imputation.

Encode factor levels for xgboost

classification
mlr3pipelines
factor encoding

The package "xgboost" unfortunately does not support handling of categorical features. Therefore, it is required to manually convert factor columns to numerical dummy features. We show how to use "mlr3pipelines" to augment the "mlr_learners_classif.xgboost" learner with an automatic factor encoding.

House prices in King County

regression
visualization
feature engineering
tuning

Use case illustrating data preprocessing and model fitting via mlr3 on the "King County House Prices" dataset.

XI Jornadas de Usuarios de R - mlr3

video
mlr3
mlr3pipelines

One hour presentation of the mlr3 ecosystem at the spanish speaking R user days.

useR! 2019 Presentation - mlr3

video
mlr3

Short presentation of the mlr3 package at the useR! 2019 in Toulouse.

useR! 2019 Presentation - mlr3pipelines

video
mlr3
mlr3pipelines

Short presentation of the mlr3pipelines package at the useR! 2019 in Toulouse.

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