Fast Interpretable Greedy-Tree Sums | JoVE Visualize

Area of Science:

Machine Learning
Medical Informatics
Computational Statistics

Background:

Modern machine learning models often lack interpretability, a crucial factor in high-stakes fields like medicine.
Traditional interpretable decision trees (e.g., CART) exhibit inductive bias against additive structures.
There is a need for interpretable models that can capture complex, additive relationships in data.

Purpose of the Study:

To introduce Fast Interpretable Greedy-Tree Sums (FIGS), a novel algorithm generalizing CART to sum multiple trees for improved interpretability and performance.
To adapt FIGS for clinical decision instruments (CDIs) through a variant called Group Probability-Weighted Tree Sums (G-FIGS), addressing medical data heterogeneity.
To theoretically analyze FIGS' disentanglement property and its efficiency in learning additive models.

Main Methods:

FIGS generalizes Classification and Regression Trees (CART) by growing multiple trees in summation, combining logical rules with addition.
G-FIGS is developed to learn CDIs, accounting for heterogeneity in medical data and improving specificity.
Bagging-FIGS, an ensemble method, is introduced to mitigate overfitting by employing variance reduction techniques similar to random forests.

Main Results:

FIGS achieves state-of-the-art prediction performance on real-world datasets.
G-FIGS derives CDIs with up to 20% improved specificity over CART, maintaining sensitivity and interpretability.
Theoretical analysis shows FIGS achieves disentanglement, enabling more efficient generalization for additive regression functions.
Bagging-FIGS demonstrates competitive performance against Random Forests and XGBoost.

Conclusions:

FIGS offers a powerful and interpretable alternative to traditional machine learning models, particularly in domains requiring explainability.
G-FIGS provides a valuable tool for developing robust and interpretable clinical decision instruments.
The disentanglement property of FIGS contributes to a deeper theoretical understanding of tree-sum models and their generalization capabilities.