What Is Regularization in Machine Learning? Discouraging Overly Complex Solutions
The broad category of techniques that intentionally constrain a model during training, favoring simpler, more generalizable solutions over ones that merely minimize training error.
Intentionally constraining what a model is allowed to learn
Regularization is the broad category of techniques that intentionally constrain a model during training — discouraging overly complex solutions — to favor ones that generalize well to new data, rather than purely and exclusively minimizing error on the training data alone.
Why minimizing training error alone isn't the actual goal
A sufficiently complex, unconstrained model can often fit its training data almost perfectly by memorizing its specific quirks rather than learning genuinely useful patterns — exactly the failure mode of overfitting. Regularization pushes back against that tendency deliberately, favoring simpler solutions even at the cost of a slightly higher training error.
Common specific techniques within this category
Weight decay (penalizing large parameter values) and dropout (randomly disabling parts of the network during training) are two of the most widely used specific regularization techniques, each discouraging overfitting through a different concrete mechanism.
Frequently Asked Questions
What is regularization in machine learning?
The broad category of techniques that intentionally constrain a model during training to favor solutions that generalize well, rather than purely minimizing error on the training data alone.
Why is minimizing training error alone not the actual training goal?
A sufficiently complex model can fit training data almost perfectly by memorizing its specific quirks rather than learning genuinely useful patterns — regularization pushes back against that tendency, favoring simpler, more generalizable solutions.