References

This page credits libraries and assets used by IAML and its documentation. For references to the methods used by a fitted candidate, see the study bibliography.

Core Libraries and Frameworks

The principal library references are:

• Scikit-learn:

  Scikit-learn: Machine Learning in Python. Available at: https://scikit-learn.org/.

• SHAP:

  SHAP (SHapley Additive Explanations): A Unified Approach to Explaining Model Predictions. Available at: https://github.com/slundberg/shap.

• CatBoost:

  CatBoost: Gradient Boosting with Categorical Features Support. Available at: https://catboost.ai/.

• Yellowbrick:

  Yellowbrick: Visual Analysis and Diagnostic Tools for Machine Learning. Available at: https://www.scikit-yb.org/.

• cuDF (RAPIDS AI):

  cuDF: GPU DataFrame Library for Data Manipulation. Available at: https://rapids.ai/.

• NLTK:

  NLTK (Natural Language Toolkit): Tools for Working with Human Language Data. Available at: https://www.nltk.org/.

Typography

The IAML wordmark uses Bebas Neue, designed by Ryoichi Tsunekawa, under the SIL Open Font License 1.1.

Documentation animation

The Discover IAML and Customize IAML pages use GSAP and ScrollTrigger under the GSAP Standard License. The library and its license notice are bundled with the documentation.