Getting started

msitrees is a set of machine learning models based on minimum surfeit and inaccuracy decision tree algorithm. The main difference to other CART methods is, that there is no hyperparameters to optimize for base learner. Tree is regularized internally to avoid overfitting by design. Quoting authors of the paper:

To achieve this, the algorithm must automatically understand when growing the decision tree adds needless complexity, and must measure such complexity in a way that is commensurate to some prediction quality aspect, e.g., inaccuracy. We argue that a natural way to achieve the above objectives is to define both the inaccuracy and the complexity using the concept of Kolmogorov complexity.

Contents

• Installation guide
  • With pip
  • From source
• Examples
  • Minimal example
  • Model preservation
  • Zero hyperparameter based approach
• API docs
  • Tree API
  • Ensemble API