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
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.
Hyperparameter
Configuration settings set before training that control the learning process, not learned from data.
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