MLPack

Language: CPP

Machine Learning

MLPack was first released in 2011 as a high-performance machine learning library written in C++. Its design philosophy focuses on speed, scalability, and clean API design. Built on top of Armadillo for linear algebra, MLPack is used in academia and industry for research and production, providing algorithms ranging from classification and regression to deep learning and clustering.

MLPack is a fast, flexible, and scalable C++ machine learning library. It provides a wide range of machine learning algorithms and data science tools with a focus on high performance and ease of use, while also offering bindings for Python, Julia, and other languages.

Installation

linux: sudo apt install libmlpack-dev

mac: brew install mlpack

windows: vcpkg install mlpack or build from source using CMake

Usage

MLPack provides supervised learning (decision trees, logistic regression, random forests), unsupervised learning (k-means, EM clustering), deep learning, reinforcement learning, dimensionality reduction, and optimization algorithms.

K-means clustering

#include <mlpack/methods/kmeans/kmeans.hpp>
#include <armadillo>
#include <iostream>

int main() {
    arma::mat data;
    data.load("data.csv");

    mlpack::kmeans::KMeans<> k;
    arma::Row<size_t> assignments;
    k.Cluster(data, 3, assignments);

    assignments.print("Cluster assignments:");
    return 0;
}

Loads data from CSV, runs k-means clustering with 3 clusters, and prints assignments.

Logistic regression

#include <mlpack/methods/logistic_regression/logistic_regression.hpp>

mlpack::regression::LogisticRegression<> lr(trainData, trainLabels, 0.5);
arma::Row<size_t> predictions;
lr.Classify(testData, predictions);

Trains a logistic regression model and uses it to classify test data.

Random forest classifier

#include <mlpack/methods/random_forest/random_forest.hpp>

mlpack::tree::RandomForest<> rf(trainData, trainLabels, 10, 5);
arma::Row<size_t> results;
rf.Classify(testData, results);

Trains a random forest with 10 trees and depth 5.

Principal Component Analysis (PCA)

#include <mlpack/methods/pca/pca.hpp>

mlpack::pca::PCA pca;
arma::mat transformed;
pca.Apply(data, transformed, 2);

Reduces data dimensions from N to 2 using PCA.

Reinforcement learning (DQN)

// mlpack provides deep reinforcement learning APIs like DQN and policy gradients

Supports reinforcement learning algorithms for training agents in environments.

Error Handling

arma::mat load failure: Ensure the dataset file exists and is in a valid format (CSV/TSV/Armadillo binary).

Model convergence issues: Adjust hyperparameters such as learning rate, iterations, or regularization strength.

High memory usage: Use sparse matrix types (`arma::sp_mat`) when working with sparse data.

Best Practices

Use Armadillo matrices as input/output since MLPack is built on top of Armadillo.

Scale and normalize datasets before training ML models.

Use parallelism (OpenMP) for large datasets to improve performance.

Leverage MLPack’s command-line tools (`mlpack_knn`, `mlpack_kmeans`) for quick experiments before coding.

Choose appropriate regularization parameters to prevent overfitting in supervised models.

Official Docs Github