Term tag: statistics

A/B Testing

A/B testing is the randomized experiment that decides whether a model, prompt, or feature change truly improves a live metric — or just moved on noise.

Causal Reasoning

Why a model trained on observational data cannot tell cause from correlation, what that costs when you act on it, and what actually identifies one.

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.

Conformal Prediction

A model-agnostic, distribution-free method that turns any prediction into a set guaranteed to contain the true answer at a chosen rate, such as 90%.

Data Analysis

What the work consists of: framing a question, looking before aggregating, choosing a summary that does not lie, and turning a number into a decision.

Information Gain

Entropy worked from scratch, one split computed by hand in bits, and why a unique ID column scores the maximum possible information gain.

Low Variance

Low variance means values sit close to their average — and for a model, that retraining it on fresh data barely moves its predictions. Worked on numbers.

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.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) reduces dimensions by projecting data onto orthogonal directions of maximum variance, ranked by the variance each keeps.

Time Series

Data points indexed in time order, where the sequence matters and rows aren't independent — analyzed to forecast future values or flag anomalies.