Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
In-depth explanation
In 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
Related terms
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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