Term tag: Data Science

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.

Data Processing

What happens to raw data before a model can use it: ingestion, validation, transformation, ETL vs ELT, batch vs streaming, and what silently breaks.

Feature Scaling

Feature scaling puts numeric features on comparable ranges so no variable dominates by its units alone — why distance- and gradient-based models need it.

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.

Machine Learning Operations (MLOps)

Why shipping a model is not like shipping software: the artifact has three inputs — code, data and trained weights — and only one of them lives in git.

Naive Bayes

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

Ontologies

An ontology is a formal, machine-readable model of a domain — classes, properties and logical axioms a computer can reason over, not just a taxonomy of labels.

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.

Text Analysis

Turning unstructured text into structured data — the concrete NLP task catalog: classification, sentiment analysis, entity extraction and topic modeling.

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.