Term tag: supervised learning

Active Learning

Active learning lets a model choose which unlabelled examples to get labelled next, hitting target accuracy with far fewer labels than random labelling.

Class Imbalance

Why a fraud model can score 99% accuracy and catch nothing — the imbalance trap, why precision/recall/PR-AUC replace accuracy, and how to fix it.

Classification (CLF)

Accuracy is usually the wrong metric. What a confusion matrix, precision, recall and the decision threshold really tell you about a classifier.

Decision Trees (DT)

How a decision tree actually chooses each split: Gini impurity worked by hand, why depth drives overfitting, and what a single tree cannot represent.

k-Nearest Neighbors (kNN)

How k-Nearest Neighbors classifies a point by majority vote of its k closest neighbours: choosing k, the distance metric, and the curse of dimensionality.

Logistic Regression (LR)

Logistic regression turns weighted evidence into a probability with the sigmoid: why the name says regression, how to read coefficients, and when to use it.

Machine Learning (ML)

How a machine finds a rule nobody wrote: a line fitted to four points by hand, loss falling from 433.5 to 6.38 in three steps, and why 98% accuracy lies.

Naive Bayes

A probabilistic classifier applying Bayes' theorem under a deliberately false feature-independence assumption — fast, data-light, a strong text baseline.

Precision and Recall

Precision, recall, F1 and the confusion matrix worked through one imbalanced example — and why the model with the best accuracy can catch nothing at all.

Regression

The supervised-learning task of predicting a continuous number — a price, a temperature, a duration — from input features, in contrast to classification.

Supervised Learning

Training a model on labeled input-output pairs so it learns a mapping it can apply to new, unlabeled inputs.