Machine Learning
Definition
Machine learning uses data so that models derive patterns for predictions or decisions.
Methods where models derive patterns from data for predictions, classifications, or decisions.
Simply explained
Learning a rule from many training examples instead of writing an instruction for each individual case.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
A model predicts failure risks from historical machine data.
Limits and pitfalls
- Confusing correlation with causation or ignoring data leakage and drift.
Origin & history
Arthur Samuel coined the term in 1959; the field includes supervised, unsupervised, and reinforcement learning methods.
Use in mid-sized companies
- Possible application: A domain and data team defines a forecasting task and prepares suitable historical data. It separates training and evaluation and checks for drift during later operation.
Frequently asked questions
What does Machine Learning mean?
Methods where models derive patterns from data for predictions, classifications, or decisions.
Which mistakes should be avoided?
Confusing correlation with causation or ignoring data leakage and drift.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: