Machine Learning

A practical guide to classification metrics — accuracy, precision, recall, F1 and ROC-AUC — with a decision process for choosing the right one for balanced vs imbalanced data.

The ROC curve plots true positive rate vs false positive rate across all thresholds; AUC summarises it as one number. Learn how to read them, what a good AUC is, and ROC-AUC vs PR-AUC.

The F1 score is the harmonic mean of precision and recall: F1 = 2·(P·R)/(P+R). Learn why it uses a harmonic mean, when to use it, F-beta, and macro vs micro vs weighted F1.

Precision is TP/(TP+FP) and recall is TP/(TP+FN). Learn the difference, the precision–recall trade-off, and which to prioritise for imbalanced problems like fraud, disease and spam.

A confusion matrix compares predicted vs actual labels across four cells — TP, FP, TN, FN — and every classification metric (accuracy, precision, recall, F1) is derived from them.

Supervised vs unsupervised vs reinforcement learning is the first big map you need when learning machine learning: three families of

Sklearn logistic regression is the fastest way to train a working classifier in Python: with scikit-learn you can import one

The logistic regression decision boundary is the exact place where the model stops favoring one class and starts favoring the

Logistic regression gradient descent is the iterative procedure that actually trains a logistic regression model — it starts with random

This logistic regression example walks through a complete classification problem from raw data to a final prediction, using the friendliest