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<!DOCTYPE html>
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    <title>ML Library in C++</title>
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        <h1>A Machine Learning Library in C++</code></h1>
        <p class="meta">By Mateus Riff • Feb. 2025</p>
        <div class="content">
            <h2>C++ Machine Learning Library</h2>
            <p>
                This project is a simple yet effective Machine Learning library implemented in C++ using Object-Oriented Programming (OOP) principles.
                It provides an abstraction layer for regression models, allowing users to easily train and use them for predictive tasks.
            </p>

            <h3>OOP Principles in Action</h3>
            <p>
                The library follows OOP principles such as encapsulation, inheritance, abstraction, and polymorphism. The core class, <code>Regression</code>,
                is an abstract base class that defines shared attributes and methods for its derived classes: <code>LinearRegression</code> and <code>LogisticRegression</code>.
            </p>
            <ul>
                <li><strong>Encapsulation:</strong> The base class holds common data members such as model parameters and learning functions.</li>
                <li><strong>Inheritance:</strong> The concrete regression classes inherit from <code>Regression</code>, ensuring code reuse and structure.</li>
                <li><strong>Abstraction:</strong> Clients only interact with the interface without needing to know implementation details.</li>
                <li><strong>Polymorphism:</strong> Each derived class implements its own version of cost computation and prediction methods.</li>
            </ul>
            <p>
                For more information, you can read the <a href="https://docs.google.com/document/d/1avqy3lKtND2OwwKUXkevZPl6yp-b8wLQ7Cvuwg35exU/edit?usp=sharing">full project report</a>.
            </p>

            <h3>Usage</h3>
            <p>
                The library can be built using CMake and easily integrated into other C++ projects. Clients can create, train, and use models with just a few lines of code:
            </p>
            <pre>
#include &lt;vector&gt;
#include "ml_lib/ml.h"

int main() {
    ml::LinearRegression model;
    model.fit(X, y, 0.01f, 1000);
    float prediction = model.predict({5.0f, 6.0f});
    return 0;
}
            </pre>

            <h3>Evaluation</h3>
            <p>
                The library's models were tested against <code>scikit-learn</code> implementations. The <code>ml_cpp</code> Linear Regression model achieved a lower Mean Squared Error (MSE) than the equivalent in <code>scikit-learn</code>, demonstrating its efficiency. However, the Logistic Regression model performed worse, highlighting areas for potential improvement.
            </p>

            <h3>Project Demo Video (Portuguese)</h3>
            <div>
                <iframe width="800" height="450" src="https://www.youtube.com/embed/QAVnnVag_t4" frameborder="0" allowfullscreen></iframe>
            </div>

            <h3>Contributors & Resources</h3>
            <p>
                The project was developed by Mateus da Nóbrega Riff. It was guided by the course <a href="https://www.coursera.org/learn/machine-learning" target="_blank">Supervised Machine Learning: Regression and Classification</a>, and OpenAI models were occasionally consulted for troubleshooting and documentation.
            </p>
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