Supervised Learning
A machine learning method in which models are trained using labelled data.
Supervised Learning
What Is Supervised Learning
Supervised learning is a machine learning approach where models are trained on labelled examples. Each training input is paired with a known output, allowing the model to learn patterns that map inputs to expected results.
How Supervised Learning Works
During training, the model predicts outputs for labelled inputs and compares predictions with ground truth labels. An optimisation process updates model parameters to reduce prediction error over many iterations.
- prepare labelled datasets
- train a model with a defined objective function
- validate performance on unseen samples
- deploy and monitor model behaviour in production
Types of Supervised Learning (classification and regression)
Supervised learning is commonly split into classification and regression tasks. Classification predicts discrete categories, while regression predicts continuous numeric values.
- classification: spam detection, intent labels, language identification
- regression: score prediction, quality estimation, demand forecasting
Role in Machine Translation and NLP
In NLP and translation, supervised learning powers tasks such as sequence tagging, quality estimation, and bilingual mapping. Labelled corpora allow systems to learn grammar, terminology usage, and context sensitive predictions that improve translation workflows.
Examples of Supervised Learning Models
Typical supervised models include:
- logistic regression and support vector machines
- decision trees and random forests
- neural networks, including transformer based architectures
- sequence models for tagging and translation quality tasks