MachineLearning

Offers a unified interface for training and prediction across all algorithms, as shown in the API introduction example with fit() and predict(), simplifying experimentation and model comparison.

Algorithms are written natively in Julia without external dependencies, ensuring compatibility and leveraging Julia's performance for in-memory workflows, as emphasized in the package description.

Includes utilities for train/test splitting, cross-validation, and experiment management, reducing boilerplate code for model assessment, as listed in the 'Other Helpers' section.

Provides clear, basic implementations of algorithms like decision trees and neural networks in Julia, making it useful for learning ML concepts directly from the source code.