Subject
6 entries
Classification
Bookmarks
Batch classification with Instructor
An Instructor example showing how to classify multiple items in a single batched LLM call using Pydantic schemas — more efficient than sequential one-at-a-time classification, and type-safe by construction.
An Introduction to Statistical Learning (2nd Edition)
The second edition of James, Witten, Hastie, and Tibshirani's canonical intro-level statistical learning textbook, updated in 2021 to include deep learning, survival analysis, and multiple testing. It sits between undergraduate statistics and the more demanding Elements of Statistical Learning — the best entry point for practitioners who want rigorous but accessible ML foundations.
The ROC Curve Explained
A visual explanation of the Receiver Operating Characteristic (ROC) curve and AUC for binary classifier evaluation. One of the clearest introductions to the concept for practitioners learning to assess model performance beyond accuracy.
Calibrating Classifier Probabilities
Daniel Nee's post on calibrating classifier output probabilities — the underappreciated problem that a model's predicted probability of 0.8 doesn't always mean there's an 80% chance of the positive class. Essential reading before using model outputs for decision-making.
Advantages of Different Classification Algorithms
Quora thread on the tradeoffs between classification algorithms — naive Bayes, SVM, decision trees, logistic regression, k-NN, and neural networks. A practical reference for choosing the right algorithm given your data characteristics and constraints.
Machine Learning and Link Spam: My Brush With Insanity
A practitioner's account of applying machine learning to link spam detection at SEOMoz — the messy reality of building a classifier with noisy labels, adversarial inputs, and shifting distributions. An honest account of ML in production before MLOps was a field.