Equality of Odds
Explore equality of odds, a metric used to quantify unfairness and remove bias from machine learning models.
Visual explanations of core machine learning concepts
Machine Learning University (MLU)
is an education initiative from Amazon designed to teach machine
learning theory and practical application.
As part of that goal,
MLU-Explain exists to teach important
machine learning concepts through visual essays in a fun,
informative, and accessible manner.
Explore Published Articles...
Cross-Validation
K-Fold Cross-Validation: a resampling technique to help improve estimates of test error rates compared to a simple validation set.
Precision & Recall
When it comes to evaluating classification models, accuracy is often a poor metric. This article covers two common alternatives, Precision and Recall, as well as the F1-score and Confusion Matrices.
Random Forest
Learn how the majority vote and well-placed randomness can extend the decision tree model to one of machine learning's most widely-used algorithms, the Random Forest.
Decision Trees
Explore one of machine learning's most popular supervised algorithms: the Decision Tree. Learn how the tree makes its splits, the concepts of Entropy and Information Gain, and why going too deep is problematic.
The Bias Variance Tradeoff
Understand the tradeoff between under- and over-fitting models, how it relates to bias and variance, and explore interactive examples related to LOESS and KNN.
Double Descent: A Visual Introduction
Meet the double descent phenomenon in modern machine learning: what it is, how it relates to the bias-variance tradeoff, the importance of the interpolation regime, and a theory of what lies behind.