5 posts · page 1 of 1
Understand how the k-nearest neighbors algorithm classifies and regresses by looking at similar examples, when it works well, and how to tune k and distance metrics for real problems.
A practical walkthrough of the Naive Bayes classifier: how it uses probability and a strong independence assumption to build a fast, surprisingly accurate baseline for text and tabular data.
Decode precision, recall, F1, and accuracy with concrete intuition, threshold tuning, and PR vs ROC curve guidance for imbalanced data.
An intuitive walkthrough of support vector machines, the kernel trick, and when SVMs still make sense in a world dominated by gradient boosted trees and neural networks.
Learn how logistic regression turns a linear score into a probability, how to train it with scikit-learn, and how to evaluate binary classifiers using ROC-AUC.