AI GlossaryRegressionENTRY — Machine Learning
[Machine Learning]
Regression.
Predicting a continuous numerical value based on input features.
In-depth explanation
01Regression is a supervised learning task where the output is a continuous number rather than a discrete category. Linear regression fits a straight line, while more complex methods like polynomial regression, random forest regression, and neural networks can capture non-linear relationships. Evaluation metrics include MSE, RMSE, and R-squared.
Examples
02EX. 01
House price prediction
EX. 02
Stock price forecasting
EX. 03
Age estimation
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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