Return Coefficients of the Linear Regression Model Using MLPack C++

Introduction

MLpack is a powerful C++ machine learning library that includes a number of algorithms for linear regression, including Ordinary Least Squares (OLS) regression, Ridge regression, Lasso regression, Elastic Net regression, and others.

Using the Code

To calculate the coefficients of a linear regression model using MLpack, you can follow these steps:

1. First, you need to prepare your data. This involves loading your data into an appropriate data structure that MLpack can work with, such as an arma::mat or an mlpack::data::Dataset object.

2. Next, you need to define your linear regression model. You can do this using the mlpack::regression::LinearRegression class. This class takes as input your training data and the regularization parameter (if applicable), and it fits the linear regression model to the data using the specified algorithm.

3. Once you have defined your model, you can train it on your data using the mlpack::regression::LinearRegression::Train() method.

4. After training the model, you can extract the coefficients of the linear regression model using the mlpack::regression::LinearRegression::Parameters() method. This will return an arma::vec object containing the coefficients of the linear regression model.

Here is some sample code that demonstrates how to calculate the coefficients of a linear regression model using MLpack:

#include <mlpack/methods/linear_regression/linear_regression.hpp>
#include <mlpack/core/data/load.hpp>
#include <mlpack/core/math/random.hpp>

using namespace mlpack::regression;
using namespace mlpack::data;

int main()
{
  // Load the training data.
  arma::mat X;
  data::Load("train.csv", X, true);


  // Load the target variable.
  arma::vec y;
  data::Load("target.csv", y, true);

  // Create a linear regression model and train it on the data.
  Regression::LinearRegression lr;
  lr.Train(X,y);

  // Get the coefficients of the linear regression model.
  arma::vec beta = lr.Parameters();

  // Print the coefficients.
  std::cout << "Coefficients: " << beta << std::endl;

  return 0;
}

In this example, we first load our training data into an arma::mat object X and our target variable into an arma::vec object y. We then add a column of ones to X to represent the intercept term.

Next, we create a LinearRegression object lr and train it on our data using the Train() method. Finally, we extract the coefficients of the linear regression model using the Parameters() method and print them to the console.

Points of Interest

MLpack: A powerful but little known library, I hope you can get on the right track with this piece of code.

History

• 4^th April, 2023: Initial version