AiVibeSupervised Learning
Supervised learning trains a model on labelled examples, pairs of inputs and correct outputs, so that it learns to predict the output for new inputs. It covers classification, which predicts categories, and regression, which predicts numeric values.
During training, the model's predictions are compared with the labels using a loss function, such as cross-entropy for classification or mean squared error for regression, and the parameters are adjusted to reduce it. Data is split into training, validation and test sets so that performance is measured on examples the model has not seen. Common algorithms include logistic regression, support vector machines, decision trees, gradient-boosted trees and neural networks.
Industrial examples include classifying images of parts as good or defective, predicting remaining useful life from sensor histories, estimating quality measurements from process data, known as virtual metrology, recognising text and classifying support tickets.
The main cost is labelling: labels must be accurate, consistent and representative of real operating conditions, including rare classes such as unusual defects. Class imbalance, label noise and leakage of test information into training are common pitfalls. Performance is measured with metrics such as precision, recall, F1 score or mean absolute error, chosen to match the business impact of errors.
Key points
- Learns from input-output pairs with known correct answers
- Classification predicts categories; regression predicts numbers
- Requires accurate, representative labels, often the main cost
- Performance is measured on a held-out test set
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.