mlr3gallery
Practical Tuning Series - Tuning and Parallel Processing
We teach how to run various jobs in mlr3 in parallel.
Practical Tuning Series - Build an Automated Machine Learning System
We implement a simple automated machine learning (AutoML) system which includes preprocessing, a switch between multiple learners and hyperparameter tuning.
Practical Tuning Series - Tune a Preprocessing Pipeline
We build a simple preprocessing pipeline and tune it.
Practical Tuning Series - Tune a Support Vector Machine
We demonstrate how to optimize the hyperparameters of a support vector machine (SVM).
Tuning a Complex Graph
We show how to tune a complex graph for a single task.
Integer Hyperparameters in Tuners for Real-valued Search Spaces
How to tune integer hyperparameters with tuners that can only propose real numbers.
Feature Selection on the Titanic Data Set
We give a short introduction to mlr3fselect.
Threshold Tuning for Classification Tasks
Predicting probabilities in classification tasks allows us to adjust the probability thresholds required for assigning an observation to a certain class. This can lead to improved predictive performance.
Introduction to mlr3keras - Boston Housing
This use case provides an introduction to mlr3keras via the boston housing dataset.
Comparison of Decision Boundaries of Classification Learners
Visuzalizes the decision boundaries of multiple classification learners on some artificial data sets.
A Production Example Using Plumber and Docker
We write a REST API using plumber and deploy it using Docker.
Bike Sharing Demand - Use Case
This use case provides an example on tuning and benchmarking in mlr3verse using data from Capital Bikeshare.
Target Transformations via Pipelines
We show how to do various kinds of target transformations using pipelines.
Why R? Webinar - Pipelines and AutoML with mlr3
In-depth presentation of the mlr3 and mlr3pipelines packages for the Why R? 2020 Webinar Series.
mlr3 and OpenML - Moneyball Use Case
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
We show how to engineer features using date-time variables.
A Pipeline for the Titanic Data Set - Advanced
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
This tutorial explains how to create and tune a multilevel stacking model using the mlr3pipelines package.
Pipelines, Selectors, Branches
This tutorial explains how applying different preprocessing steps on different features and branching of preprocessing steps can be achieved using the mlr3pipelines package.
Regression Chains
We show how to use mlr3pipelines to do regression chains.
Imbalanced Data Handling with mlr3
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
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!
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
This post shows how to build a Graph using the mlr3pipelines package on the "titanic" dataset.
mlr3 Basics - 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
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
In this use case, we continue working with the German credit dataset. We work on hyperparameter tuning and apply nested resampling.
Select Uncorrelated Features
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
This use case shows how to tune over multiple learners for a single task.
Impute Missing Variables
We show how to use mlr3pipelines to augment the "mlr_learners_classif.ranger" learner with automatic imputation.
Encode Factor Levels for xgboost
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
Use case illustrating data preprocessing and model fitting via mlr3 on the "King County House Prices" dataset.
XI Jornadas de Usuarios de R - mlr3
One hour presentation of the mlr3 ecosystem at the spanish speaking R user days.
useR! 2019 Presentation - mlr3
Short presentation of the mlr3 package at the useR! 2019 in Toulouse.
useR! 2019 Presentation - mlr3pipelines
Short presentation of the mlr3pipelines package at the useR! 2019 in Toulouse.