CORElearn
CORElearn offers a robust suite of C++ machine learning algorithms integrated with R, facilitating various classification and regression techniques. It encompasses models like random forests, kNN, and naive Bayes, with predictive outputs explainable via the 'ExplainPrediction' package. Additionally, it features advanced attribute evaluation methods and supports multithreaded execution through OpenMP. For more information, visit https://CRAN.R-project.org/package=CORElearn.
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Top CORElearn Features
- C++ implementation for efficiency
- R interface for flexibility
- Variety of learning techniques
- Classification and regression support
- Explainable predictions with visualization
- Advanced feature evaluation methods
- Multiple impurity evaluation functions
- Constructive induction for enhanced models
- OrdEval for ordinal feature analysis
- Kano model integration for insights
- Parallel multithreaded execution
- Relief algorithm variants included
- Feature selection and discretization
- Random forests and kNN support
- Naive Bayes classification capability
- Locally weighted regression options
- Comprehensive top-level documentation
- User-friendly interface for data scientists
- Customizable predictive modeling solutions
- Flexible integration with R ecosystem.
