Machine Learning in R - Next Generation
Column Information for Backend
Container for Benchmarking Results
Generate a Benchmark Grid Design
Benchmark Multiple Learners on Multiple Tasks
Convert to a Prediction
Convert to BenchmarkResult
Convert to a Task
Create a Data Backend
Convert to a Learner
Convert to a Measure
Convert to a Classification Prediction
PredictionData
Convert to a Regression Prediction
Column Auto-Converter
Convert to ResampleResult
Convert to a Resampling
Convert to ResultData
Convert to a Classification Task
Convert to a Regression Task
Convert to an Unsupervised Task
Convert a Task from One Type to Another
DataBackend
DataBackend for data.table
DataBackend for Matrix
Get the Default Measure
Stack for Hot Start Learners
Install (Missing) Packages
False Negatives
Learner Class
Classification Learner
Regression Learner
(Un)marshal a Learner
Measure Class
Classification Measure
Regression Measure
Similarity Measure
Assertion for mlr3 Objects
Classification Learner for Debugging
Featureless Classification Learner
Classification Tree Learner
Regression Learner for Debugging
Featureless Regression Learner
Regression Tree Learner
Dictionary of Learners
Akaike Information Criterion Measure
Bayesian Information Criterion Measure
Classification Accuracy
Area Under the ROC Curve
Balanced Accuracy
Binary Brier Score
Classification Error
Cost-sensitive Classification Measure
Diagnostic Odds Ratio
F-beta Score
False Discovery Rate
False Negative Rate
False Omission Rate
False Positives
False Positive Rate
Log Loss
Multiclass Brier Score
Multiclass AUC Scores
Multiclass AUC Scores
Multiclass AUC Scores
Multiclass AUC Scores
ResultData
Matthews Correlation Coefficient
Negative Predictive Value
Positive Predictive Value
Area Under the Precision-Recall Curve
Positive Predictive Value
True Positive Rate
True Positive Rate
True Negative Rate
True Negatives
True Negative Rate
True Positives
True Positive Rate
Debug Measure for Classification
Elapsed Time Measure
Measure Internal Validation Score
Task Class
Out-of-bag Error Measure
Bias
Kendall's tau
Mean Absolute Error
Classification Task
Mean Absolute Percent Error
Max Absolute Error
Median Absolute Error
Median Squared Error
Mean Squared Error
Mean Squared Log Error
Percent Bias
Relative Absolute Error
Root Mean Squared Error
Root Mean Squared Log Error
Root Relative Squared Error
Relative Squared Error
R Squared
Sum of Absolute Errors
Symmetric Mean Absolute Percent Error
Spearman's rho
Sum of Squared Errors
Selected Features Measure
Jaccard Similarity Index
Phi Coefficient Similarity
Dictionary of Performance Measures
Reflections for mlr3
Bootstrap Resampling
Custom Cross-Validation
Custom Resampling
Cross-Validation Resampling
Holdout Resampling
Insample Resampling
Leave-One-Out Cross-Validation
Repeated Cross-Validation Resampling
Subsampling Resampling
Dictionary of Resampling Strategies
Syntactic Sugar for Object Construction
2D Normals Classification Task Generator
Cassini Classification Task Generator
Circle Classification Task Generator
Friedman1 Regression Task Generator
Moons Classification Task Generator
Simplex Classification Task Generator
Smiley Classification Task Generator
Spiral Classification Task Generator
XOR Classification Task Generator
Dictionary of Task Generators
Boston Housing Regression Task
Wisconsin Breast Cancer Classification Task
German Credit Classification Task
Iris Classification Task
Motor Trend Regression Task
Palmer Penguins Data Set
Pima Indian Diabetes Classification Task
Sonar Classification Task
Spam Classification Task
Wine Classification Task
Zoo Classification Task
Dictionary of Tasks
Documentation of mlr3 test helpers
mlr3: Machine Learning in R - Next Generation
Manually Partition into Training and Test Set
Set the Number of Threads
Predict Method for Learners
Abstract Prediction Object
Prediction Object for Classification
Convert to PredictionData
Prediction Object for Regression
Objects exported from other packages
Resample a Learner on a Task
Container for Results of resample()
Resampling Class
TaskGenerator Class
Regression Task
Supervised Task
Unsupervised Task
Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Useful links