Argonz-ML is a lightweight machine learning library designed for JavaScript developers. This library makes it easy to implement various machine learning algorithms without the complexity often found in other libraries. It is published on npmjs, allowing for simple installation and integration into your projects.
For the latest releases, visit this link. You can download the necessary files and execute them as needed.
- Easy to Use: Simple API for quick integration.
- Multiple Algorithms: Supports decision trees, linear regression, logistic regression, random forests, SVM, and XGBoost.
- Lightweight: Minimal footprint makes it suitable for both web and Node.js applications.
- Well Documented: Comprehensive documentation to guide you through installation and usage.
- Community Driven: Open to contributions and suggestions from users.
To install Argonz-ML, use npm:
npm install argonz-mlThis command installs the library and its dependencies, allowing you to start using it in your projects right away.
Here’s a basic example of how to use Argonz-ML:
const { DecisionTree } = require('argonz-ml');
// Sample data
const data = [
{ features: [1, 2], label: 'A' },
{ features: [2, 3], label: 'B' },
];
// Create and train the model
const model = new DecisionTree();
model.train(data);
// Make a prediction
const prediction = model.predict([1.5, 2.5]);
console.log(prediction); // Outputs: 'A' or 'B'This example demonstrates how to create a decision tree model, train it with data, and make predictions.
Argonz-ML includes several machine learning algorithms:
Decision trees are a popular choice for classification tasks. They work by splitting the data into branches based on feature values.
This algorithm predicts a continuous output based on the input features. It is simple and effective for many problems.
Logistic regression is used for binary classification tasks. It estimates the probability of a class label based on input features.
Random forests combine multiple decision trees to improve accuracy and reduce overfitting.
SVMs are effective for both linear and non-linear classification tasks. They find the hyperplane that best separates the classes.
XGBoost is an optimized gradient boosting library that is efficient and scalable. It is widely used in competitions due to its performance.
Here are a few more examples of how to use Argonz-ML:
const { LinearRegression } = require('argonz-ml');
// Sample data
const data = [
{ features: [1], label: 2 },
{ features: [2], label: 3 },
{ features: [3], label: 5 },
];
// Create and train the model
const model = new LinearRegression();
model.train(data);
// Make a prediction
const prediction = model.predict([4]);
console.log(prediction); // Outputs: 6const { LogisticRegression } = require('argonz-ml');
// Sample data
const data = [
{ features: [1, 1], label: 0 },
{ features: [2, 2], label: 1 },
];
// Create and train the model
const model = new LogisticRegression();
model.train(data);
// Make a prediction
const prediction = model.predict([1.5, 1.5]);
console.log(prediction); // Outputs: 0 or 1We welcome contributions to Argonz-ML! If you have ideas for improvements or new features, please fork the repository and submit a pull request.
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push your changes to your fork.
- Open a pull request.
For any questions or discussions, feel free to open an issue in the repository.
This project is licensed under the MIT License. See the LICENSE file for details.
For more information, check the Releases section.