Classification vs Data classification

Why they differ

Same word, different job. Data classification is people labelling files by how secret they are, not a machine sorting inputs.

Classification

Machine learning basics

Teaching a computer to sort each new case into one of a fixed set of groups, such as approve or reject, or real attack or false alarm.

Formal

A task in machine learning where the model learns from labelled examples to give each new input one label from a known, fixed list, often together with a score for how sure it is.

In plain English

Like a coin-sorting machine at a bank; every coin drops into the slot for its value, and there is no slot for a coin it has never seen.

In practice

At a shipping company, a model trained on thousands of invoices a bookkeeper had already sorted puts each new invoice into one of 40 cost types, such as fuel or harbour fees, and sends the unsure ones back to her.

Why it matters

No model sorts every case right, and wrong calls differ in cost (a missed attack usually costs more than a false alarm), so someone must decide which mistakes to accept and where a person checks.

Data classification

Fundamentals

Sorting data by how sensitive it is - for example public, internal or confidential.

Formal

The practice of labelling information by how much harm its loss or leak would cause, so that each level gets matching rules for handling and protection.

In plain English

Like sorting post into postcards, letters and registered mail - each is handled with a different level of care.

In practice

In a Danish municipality, council meeting agendas are marked “public”, the staff handbook “internal” and case files about citizens' health “confidential”, which means they may only be sent by secure email.

Why it matters

Protecting everything to the highest level is too costly, and protecting everything to the lowest is dangerous; classification tells you where to put the effort.