bst: Gradient Boosting
The bst package implements a functional gradient descent algorithm tailored for various convex and non-convex loss functions, addressing both classical and robust regression and classification challenges. It serves as a versatile tool for machine learning practitioners, enhancing predictive modeling capabilities. More information can be found at https://CRAN.R-project.org/package=bst.
Top bst: Gradient Boosting Alternatives
RPMM
RPMM is a sophisticated machine learning software utilizing a Recursively Partitioned Mixture Model to analyze Beta and Gaussian mixtures.
FRBS
FRBS is a robust implementation of various learning algorithms tailored for classification and regression tasks using fuzzy rule-based systems.
rgenoud
rgenoud is an advanced machine learning software that integrates a genetic algorithm with a derivative optimizer, enabling efficient global optimization.
RGP
The 'rgp' package is a specialized machine learning software designed for genetic programming.
partykit
Partykit offers a robust toolkit for representing, summarizing, and visualizing tree-structured regression and classification models.
CORElearn
CORElearn offers a robust suite of C++ machine learning algorithms integrated with R, facilitating various classification and regression techniques.
mboost
It accommodates user-defined loss functions and base-learners for fitting generalized linear, additive, and interaction models...
svmpath
This advanced machine learning software enables users to explore model performance across various regularization parameters...
maptree
It provides users with example data to streamline the process and enhance model visualization...
tgp
It encompasses various models, including Bayesian linear models and stationary GPs, while offering visualizations like...
Cubist
It effectively combines the interpretability of decision rules with the adaptability of instance-based learning, making...
oblique.tree
It enables users to construct trees that can split data along oblique hyperplanes, enhancing predictive...
C5.0: Decision Trees and Rule-Based Models
It excels in pattern recognition, offering enhanced performance and flexibility for data analysis tasks...
FPS
It enables users to visualize group separations using discriminant projections and assess cluster stability...
Rmalschains
This methodology, rooted in the research of Molina et al., is designed to efficiently tackle...
