R mlr
R mlr is an R package that provides a unified interface for classification, regression, survival analysis and clustering. It features automated machine learning with hyperparameter tuning, flexible feature preprocessing and model ensemble capabilities.
R mlr: Unified Machine Learning Package
R mlr is an R package that provides a unified interface for classification, regression, survival analysis and clustering. It features automated machine learning with hyperparameter tuning, flexible feature preprocessing and model ensemble capabilities.
What is R mlr?
R mlr is an R package that provides a unified interface for classification, regression, survival analysis and clustering. It features:
- Automated machine learning with hyperparameter tuning using various search strategies like grid search, random search, Bayesian optimization etc.
- Support for over 60 learners from regression, classification, survival analysis, cluster analysis etc.
- Flexible feature preprocessing with missing value imputation, scaling, generating interactions etc.
- Model ensemble capabilities like stacking and bagging
- Built-in resampling for robust performance estimation like cross-validation and bootstrapping
- Feature selection and hyperparameter tuning capabilities
- Easy integration with other R packages
Some key advantages of mlr are:
- Unified interface to train and predict on various models
- Automating machine learning pipeline consisting of preprocessing, model training, tuning and evaluation
- Easily benchmark different models
- Ensemble modeling capabilities
- Detailed logging mechanisms
mlr is a popular package used widely for automated machine learning and model building in R.
R mlr Features
Features
- Unified interface for machine learning tasks like classification, regression, survival analysis and clustering
- Automated machine learning with hyperparameter tuning
- Flexible feature preprocessing capabilities
- Model ensemble capabilities
- Supports a wide range of machine learning algorithms
- Visualizations for analyzing machine learning models and results
Pricing
- Open Source
Pros
Simplifies machine learning workflow in R
Automates tedious tasks like hyperparameter tuning
Flexible and customizable for different use cases
Modular design allows swapping components easily
Well documented
Cons
Less user-friendly than GUI-based tools
Steep learning curve for new R users
Advanced features have a complexity cost
Less support compared to commercial solutions
Official Links
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