Core Concepts
Variance
In machine learning, variance measures how much a model's predictions change when trained on different subsets of data. High variance indicates the model is capturing noise rather than signal, leading to overfitting.
The bias-variance tradeoff is a foundational concept: reducing variance often increases bias and vice versa. Regularization, cross-validation, and ensemble methods help manage variance.
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Related Terms
Core Concepts
Overfitting
Model learns detail and noise in training data too thoroughly, reducing generalization.
Core Concepts
Bias
Preconceived notions in AI models that affect decision-making and fairness.
Techniques & Methods
Regularization
Techniques that prevent overfitting by penalizing model complexity during training.