Visuzalizes the decision boundaries of multiple classification learners on some artificial data sets.
We write a REST API using plumber and deploy it using Docker.
This use case provides an example on tuning and benchmarking in mlr3verse using data from Capital Bikeshare.
We show how to do various kinds of target transformations using pipelines.
In-depth presentation of the mlr3 and mlr3pipelines packages for the Why R? 2020 Webinar Series.
This use case shows how to make use of OpenML data and how to impute missing values in a ML problem.
We show how to engineer features using date-time variables.
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.
This tutorial explains how to create and tune a multilevel stacking model using the mlr3pipelines package.
This tutorial explains how applying different preprocessing steps on different features and branching of preprocessing steps can be achieved using the mlr3pipelines package.
We show how to use mlr3pipelines to do regression chains.
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.
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.
Basic ML operations on iris: Train, predict, score, resample and benchmark. A simple, hands-on intro to mlr3.
This post shows how to build a Graph using the mlr3pipelines package on the "titanic" dataset.
In this use case, we teach the basics of mlr3 by training different models on the German credit dataset.
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.
In this use case, we continue working with the German credit dataset. We work on hyperparameter tuning and apply nested resampling.
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.
This use case shows how to tune over multiple learners for a single task.
We show how to use mlr3pipelines to augment the "mlr_learners_classif.ranger" learner with automatic imputation.
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.
Use case illustrating data preprocessing and model fitting via mlr3 on the "King County House Prices" dataset.
One hour presentation of the mlr3 ecosystem at the spanish speaking R user days.
Short presentation of the mlr3 package at the useR! 2019 in Toulouse.
Short presentation of the mlr3pipelines package at the useR! 2019 in Toulouse.