ML Fundamentals

Association Rules

Finding "what goes with what" patterns in data, like people who buy beer often buy diapers too.

Definition

A rule-based machine learning technique for discovering co-occurrence patterns between items in large datasets, expressed as 'if A, then B' rules. Three metrics judge each rule: support (how often the items appear together), confidence (how often B follows A), and lift (how much more likely B is given A than by chance). The Apriori and FP-Growth algorithms find these rules efficiently.

Why it matters

Association rules power the 'customers who bought this also bought that' recommendations that drive retail and e-commerce revenue. Beyond retail, they surface hidden patterns in transactions, web behavior, and medical records that guide cross-sell, layout, and inventory decisions.

Related terms in ML Fundamentals

Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
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