Term tag: classification

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.

AI in Finance

Machine learning that approves loans and blocks card fraud — where you ship the most accurate model you can defend, not the best one you can build.

Anomaly Detection (AD)

A 99%-accurate detector can still hand you a queue that is 99% false alarms. How anomaly detection scores, thresholds and ranks rare events.

Calibration

Calibration means a model's confidence matches reality: of everything it calls 80% likely, about 80% should happen — a property separate from raw accuracy.

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.

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%.

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.

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.

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.

Model Routing

Model routing sends each request to the cheapest capable model — a small model handles the easy majority and hard queries escalate to a frontier model.

Naive Bayes

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

Pattern Recognition (PR)

Pattern recognition is the automatic discovery and classification of regularities in data — the older name for what became statistical machine learning.

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.

Random Forest (RF)

A random forest averages many decision trees, each grown on a bootstrap sample and split on a random subset of features, to cut variance without adding bias.

Recommendation Systems

A recommendation system predicts what a user will want — a film, product, or song — from patterns in past behavior, and surfaces it automatically.

Supervised Learning

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

Support Vector Machines (SVM)

A classifier that separates two classes with the widest possible margin, using support vectors and the kernel trick to handle non-linear data.

Text Analysis

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