AutoML is Automated Machine Learning, referring to processes and methods to make machine learning more accessible for a general audience. This crate builds on top of the smartcore machine learning framework, and provides some utilities to quickly train and compare models.
For instance, running the following:
rust
let mut regressor = automl::regression::Regressor::default();
regressor.with_dataset(smartcore::dataset::diabetes::load_dataset());
regressor.compare_models();
print!("{}", regressor);
Will output this:
text
┌──────────────────────────┬──────────────┬─────────────┐
│ Model │ Training R^2 │ Testing R^2 │
╞══════════════════════════╪══════════════╪═════════════╡
│ LASSO Regressor │ 0.52 │ 0.49 │
├──────────────────────────┼──────────────┼─────────────┤
│ Linear Regressor │ 0.52 │ 0.48 │
├──────────────────────────┼──────────────┼─────────────┤
│ Ridge Regressor │ 0.52 │ 0.47 │
├──────────────────────────┼──────────────┼─────────────┤
│ Elastic Net Regressor │ 0.47 │ 0.45 │
├──────────────────────────┼──────────────┼─────────────┤
│ Random Forest Regressor │ 0.90 │ 0.40 │
├──────────────────────────┼──────────────┼─────────────┤
│ KNN Regressor │ 0.66 │ 0.29 │
├──────────────────────────┼──────────────┼─────────────┤
│ Support Vector Regressor │ -0.01 │ -0.03 │
├──────────────────────────┼──────────────┼─────────────┤
│ Decision Tree Regressor │ 1.00 │ -0.17 │
└──────────────────────────┴──────────────┴─────────────┘
Based on this output, you can then select the best model for the task.
Currently this crate only has AutoML features for regression and classification. This includes the following models: - Regression - Decision Tree Regression - KNN Regression - Random Forest Regression - Linear Regression - Rdige Regression - LASSO - Elastic Net - Support Vector Regression - Classification - Random Forest Classification - Decision Tree Classification - Support Vector Classification - Logistic Regression - KNN Classification