Machine Learning for Java Developers
SuperML Java Framework
Build powerful machine learning applications with Java. Our framework provides intuitive APIs, pre-built algorithms, and seamless integration for Java developers.
Why Choose SuperML Java?
Powerful Features for Java Developers
Everything you need to build production-ready machine learning applications in Java
Native Java API
Intuitive, object-oriented API designed specifically for Java developers with familiar patterns and conventions.
Pre-built Algorithms
Comprehensive collection of ML algorithms including linear regression, decision trees, neural networks, and more.
Easy Model Training
Simplified model training with automatic parameter tuning and validation built into the framework.
Data Processing
Built-in data preprocessing, feature engineering, and transformation utilities for clean ML pipelines.
Model Persistence
Save and load trained models with built-in serialization support for production deployment.
Enterprise Ready
Thread-safe, scalable design suitable for enterprise applications and high-performance computing.
Get Started in Minutes
Follow these simple steps to start building ML applications with SuperML Java
Step 1: Add Dependency
Add SuperML Java to your Maven or Gradle project:<dependency>
<groupId>org.superml</groupId>
<artifactId>superml-java</artifactId>
<version>1.0.0</version>
</dependency>
Step 2: Load Your Data
Import and prepare your dataset:Dataset data = DataLoader.fromCSV("data.csv");
data.split(0.8); // 80% train, 20% test
Step 3: Train Your Model
Create and train a machine learning model:LinearRegression model = new LinearRegression();
model.fit(data.getTrainX(), data.getTrainY());
Step 4: Make Predictions
Use your trained model for predictions:double[] predictions = model.predict(data.getTestX());
double accuracy = model.evaluate(data.getTestY());
Simple & Powerful
Write Less, Achieve More
SuperML Java provides clean, intuitive APIs that make machine learning accessible to every Java developer
Complete Example
import org.superml.*; public class MLExample { public static void main(String[] args) { // Load and prepare data Dataset data = DataLoader.fromCSV("housing.csv"); data.split(0.8); // Create and train model LinearRegression model = new LinearRegression(); model.fit(data.getTrainX(), data.getTrainY()); // Make predictions double[] predictions = model.predict(data.getTestX()); double rmse = Metrics.rmse(data.getTestY(), predictions); System.out.println("RMSE: " + rmse); } }
Comprehensive Algorithm Library
Built-in Machine Learning Algorithms
Everything you need for classification, regression, clustering, and more
Linear Regression
Simple and multiple linear regression with automatic feature scaling and regularization.
Logistic Regression
Binary and multiclass classification with built-in probability estimation.
Decision Trees
CART algorithm implementation with pruning and feature importance scoring.
Random Forest
Ensemble method combining multiple decision trees for improved accuracy.
K-Means Clustering
Unsupervised clustering algorithm with automatic centroid initialization.
Neural Networks
Multilayer perceptron with backpropagation and various activation functions.
SVM
Support Vector Machines for classification and regression tasks.
Naive Bayes
Probabilistic classifier perfect for text classification and spam filtering.
K-NN
K-Nearest Neighbors for both classification and regression problems.
Learn & Explore
Comprehensive Documentation
Everything you need to master SuperML Java
API Documentation
Complete JavaDoc documentation for all classes and methods.
Tutorials & Examples
Step-by-step tutorials and real-world examples to get you started.
Best Practices
Learn the recommended patterns and practices for production ML systems.
FAQs
Frequently Asked Questions
Get answers to common questions about SuperML Java
What Java versions are supported?
SuperML Java supports Java 8+ and is tested on Java 8, 11, 17, and 21. It works with any JVM-compatible language like Kotlin or Scala.
How does it compare to other Java ML libraries?
SuperML Java focuses on simplicity and ease of use while maintaining performance. It provides a more intuitive API compared to Weka and better Java integration than Python-based solutions.
Can I use it in production?
Yes! SuperML Java is designed for production use with thread-safe operations, efficient memory usage, and robust error handling.
Is there commercial support available?
We offer enterprise support packages including priority bug fixes, custom feature development, and dedicated technical support.
How do I contribute to the project?
SuperML Java is open source! Visit our GitHub repository to submit issues, feature requests, or pull requests. We welcome contributions from the community.
What about performance compared to native libraries?
SuperML Java is optimized for performance with efficient algorithms and memory management. For CPU-intensive tasks, it leverages native libraries where appropriate.
Ready to Build ML Applications in Java?
Join thousands of developers using SuperML Java to build intelligent applications