AutoML with SmartCore

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.

Usage

For instance, running the following:

let mut classifier = automl::classification::Classifier::default();
classifier.with_dataset(smartcore::dataset::breast_cancer::load_dataset());
classifier.compare_models();
print!("{}", classifier);

Will output this comparison of models usign cross-validation:

┌────────────────────────────────┬───────────────────┬──────────────────┐
│ Model                          │ Training Accuracy │ Testing Accuracy │
╞════════════════════════════════╪═══════════════════╪══════════════════╡
│ Random Forest Classifier       │ 1.00              │ 0.96             │
├────────────────────────────────┼───────────────────┼──────────────────┤
│ Logistic Regression Classifier │ 0.97              │ 0.95             │
├────────────────────────────────┼───────────────────┼──────────────────┤
│ Gaussian Naive Bayes           │ 0.95              │ 0.93             │
├────────────────────────────────┼───────────────────┼──────────────────┤
│ KNN Classifier                 │ 0.96              │ 0.92             │
├────────────────────────────────┼───────────────────┼──────────────────┤
│ Categorical Naive Bayes        │ 0.96              │ 0.91             │
├────────────────────────────────┼───────────────────┼──────────────────┤
│ Decision Tree Classifier       │ 1.00              │ 0.90             │
├────────────────────────────────┼───────────────────┼──────────────────┤
│ Support Vector Classifier      │ 0.87              │ 0.85             │
└────────────────────────────────┴───────────────────┴──────────────────┘

You can then train a final model using classifier.train_final_model() and perform inference using that model with the predict method.

Features

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