Author: Kevin Feasel

John Mount lays out a demonstration:

My experience is that common objective functions tend to be structured and full of coincidences and symmetries. And because they have these structures they are hard to optimize.

Let’s work up what I claim to be a fairly typical optimization problem that arises from planning or scheduling. I’ll call it the train arrival schedule problem.

Click through for the article, which includes demonstration code.

Ivan Palomares Carrascosa looks at two ways to plot classification model trade-offs:

When building machine learning models to classify imbalanced data — i.e. datasets where the presence of one class (like spam email for example) is much less frequent than the presence of the other class (non-spam email, for instance) — certain traditional metrics like accuracy or even the ROC AUC (Receiving Operating Characteristic curve and the area under it) may not reflect the model performance in realistic terms, giving overly optimistic estimates due to the dominance of the so-called negative class.

Precision-recall curves (or PR curves for short), on the other hand, are designed to focus specifically on the positive, typically rarer class, which is a much more informative measure for skewed datasets due to class imbalance.

Read on to see how these two curves can diverge and when you might trust one over the other. Ivan’s post does rely on the idea of the positive class being the smaller one and the dataset being markedly unbalanced