AI GlossaryLoss FunctionENTRY — Machine Learning
[Machine Learning]
Loss Function.
A function that measures how far model predictions are from actual values.
In-depth explanation
01Loss functions quantify prediction error, guiding the optimization process. Different tasks use different loss functions: mean squared error (MSE) for regression, cross-entropy for classification, and specialized losses for ranking or detection. The choice of loss function significantly impacts what the model learns to optimize.
Examples
02EX. 01
Mean Squared Error
EX. 02
Cross-Entropy Loss
EX. 03
Huber Loss
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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