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.
To use the latest released version of AutoML, add this to your Cargo.toml:
toml
automl = "0.2.3"
To use the bleeding edge instead, add this:
toml
automl = { git = "https://github.com/cmccomb/rust-automl" }
Running the following:
rust
let dataset = smartcore::dataset::breast_cancer::load_dataset();
let settings = automl::Settings::default_classification();
let mut classifier = automl::SupervisedModel::new_from_dataset(dataset, settings);
classifier.compare_models();
println!("{}", classifier);
will output this comparison of models using cross-validation:
text
┌────────────────────────────────┬─────────────────────┬───────────────────┬──────────────────â”
│ Model │ Time │ Training Accuracy │ Testing Accuracy │
â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡
│ Random Forest Classifier │ 835ms 393us 583ns │ 1.00 │ 0.96 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Logistic Regression Classifier │ 620ms 714us 583ns │ 0.97 │ 0.95 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Gaussian Naive Bayes │ 6ms 529us │ 0.94 │ 0.93 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Categorical Naive Bayes │ 2ms 922us 250ns │ 0.96 │ 0.93 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Decision Tree Classifier │ 15ms 404us 750ns │ 1.00 │ 0.93 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ KNN Classifier │ 28ms 874us 208ns │ 0.96 │ 0.92 │
├────────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Support Vector Classifier │ 4s 187ms 61us 708ns │ 0.57 │ 0.57 │
└────────────────────────────────┴─────────────────────┴───────────────────┴──────────────────┘
You can then train a final model using classifier.train_final_model() and perform inference using that model with the predict method.
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 - Ridge Regression - LASSO - Elastic Net - Support Vector Regression - Classification - Random Forest Classification - Decision Tree Classification - Support Vector Classification - Logistic Regression - KNN Classification - Gaussian Naive Bayes - Categorical Naive Bayes