AI GlossarySupervised LearningENTRY — Machine Learning
[Machine Learning]
Supervised Learning.
Machine learning approach where models learn from labeled training data to predict outcomes.
In-depth explanation
01In supervised learning, each training example consists of an input and a corresponding correct output (label). The model learns to map inputs to outputs by finding patterns in the labeled data. Common tasks include classification (predicting categories) and regression (predicting continuous values). It requires human effort to label the training data.
Examples
02EX. 01
Email spam detection
EX. 02
House price prediction
EX. 03
Medical diagnosis
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03Ensemble LearningCombining multiple models to produce better predictions than any single model.04FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.05Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.06Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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