Classification
Predicting which category or class an input belongs to from a set of predefined categories.
In-depth explanation
Classification is a supervised learning task where the model learns to assign inputs to discrete categories. Binary classification involves two classes (e.g., spam/not spam), while multi-class classification involves more than two (e.g., digit recognition 0-9). Common algorithms include logistic regression, decision trees, random forests, and neural networks.
Examples
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
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.
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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