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
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
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.
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