'Rcpp' Integration for the 'mlpack' Library
AdaBoost
Approximate furthest neighbor search
BayesianLinearRegression
Collaborative Filtering
DBSCAN clustering
Decision tree
Density Estimation With Density Estimation Trees
Fast Euclidean Minimum Spanning Tree
FastMKS (Fast Max-Kernel Search)
GMM Sample Generator
GMM Probability Calculator
Gaussian Mixture Model (GMM) Training
Hidden Markov Model (HMM) Sequence Generator
Hidden Markov Model (HMM) Sequence Log-Likelihood
Hidden Markov Model (HMM) Training
Hidden Markov Model (HMM) Viterbi State Prediction
Hoeffding trees
Image Converter
Kernel Density Estimation
Kernel Principal Components Analysis
k-Furthest-Neighbors Search
K-Means Clustering
k-Nearest-Neighbors Search
K-Rank-Approximate-Nearest-Neighbors (kRANN)
LARS
Simple Linear Regression and Prediction
Linear SVM is an L2-regularized support vector machine.
Large Margin Nearest Neighbors (LMNN)
Local Coordinate Coding
L2-regularized Logistic Regression and Prediction
K-Approximate-Nearest-Neighbor Search with LSH
Mean Shift Clustering
Serialize/Unserialize an mlpack model.
mlpack
Parametric Naive Bayes Classifier
Neighborhood Components Analysis (NCA)
Non-negative Matrix Factorization
Principal Components Analysis
Perceptron
Binarize Data
Descriptive Statistics
One Hot Encoding
Scale Data
Split Data
RADICAL
Random forests
Softmax Regression
Sparse Coding
R binding test
A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.
Useful links