Supervised Learning
A machine learning approach where models learn from labelled training data. The algorithm learns to map inputs to known outputs, enabling predictions on new, unseen data.
In-Depth Explanation
Supervised learning is the most common machine learning paradigm where models are trained on datasets containing input-output pairs. The "supervision" comes from labelled examples that teach the model the correct answer.
Key characteristics:
- Labelled data: Each training example has a known correct output
- Learning objective: Minimise the difference between predictions and true labels
- Generalisation: Apply learned patterns to new, unseen data
Common supervised learning tasks:
- Classification: Predicting categories (spam/not spam, sentiment)
- Regression: Predicting continuous values (prices, scores)
- Sequence labelling: Tagging sequences (named entities, POS tags)
The supervised learning workflow:
- Collect and label training data
- Split into training, validation, and test sets
- Train model to minimise prediction errors
- Evaluate on held-out test data
- Deploy for inference on new data
Business Context
Most business AI applications use supervised learning - from customer churn prediction to document classification. Quality labelled data is the key investment.
How Clever Ops Uses This
We help Australian businesses identify supervised learning opportunities and build quality training datasets for classification and prediction tasks.
Example Use Case
"Training a model on 10,000 labelled customer emails to automatically classify incoming support tickets by urgency and topic."