regularization Antonyms
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Meaning of regularization
regularization (n)
the condition of having been made regular (or more regular)
the act of bringing to uniformity; making regular
regularization Sentence Examples
- Regularization is a statistical technique used to prevent overfitting in machine learning models.
- Regularization techniques introduce a penalty term to the loss function, discouraging the model from making extreme predictions.
- L1 regularization (LASSO) promotes sparsity by adding a penalty proportional to the absolute value of the coefficients.
- L2 regularization (Ridge) reduces the variance of the model by penalizing large coefficients in proportion to their squared value.
- Elastic net regularization combines L1 and L2 regularization for improved model selection and generalization.
- Regularization hyperparameters tune the trade-off between model complexity and prediction accuracy.
- Regularization is essential in deep learning to prevent overfitting and improve generalization performance.
- Dropout is a regularization technique that randomly drops out units during training to prevent co-adaptation.
- Early stopping is a regularization strategy that monitors the validation set performance and stops training when it starts to decline.
- Regularization techniques are crucial for building robust machine learning models that generalize well to unseen data.
FAQs About the word regularization
the condition of having been made regular (or more regular), the act of bringing to uniformity; making regular
standardize, organize,normalize, coordinate, regulate, systematize, integrate, formalize, order, methodize
customize,customize, tailor, tailor,individualize, individualize,
Regularization is a statistical technique used to prevent overfitting in machine learning models.
Regularization techniques introduce a penalty term to the loss function, discouraging the model from making extreme predictions.
L1 regularization (LASSO) promotes sparsity by adding a penalty proportional to the absolute value of the coefficients.
L2 regularization (Ridge) reduces the variance of the model by penalizing large coefficients in proportion to their squared value.
