Guest
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Feb 15, 2023
2:41 AM
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Regularization in machine learning is a set of techniques that are used to prevent overfitting of the training data. Overfitting occurs when a model becomes too complex and learns to fit the training data too closely, resulting in poor performance on new, unseen data. Machine Learning Classes in Pune
Regularization works by adding a penalty term to the model's objective function, which encourages the model to have simpler coefficients and less complex relationships between the input features and the target variable. There are different types of regularization techniques, including L1 regularization (also known as Lasso), L2 regularization (also known as Ridge), and Elastic Net regularization (which combines L1 and L2 regularization).
The importance of regularization in machine learning lies in its ability to improve the generalization performance of a model. A model that is regularized is less likely to overfit the training data, which means it is more likely to perform well on new, unseen data. Regularization can also help with feature selection by penalizing the coefficients of less important features, which can help simplify the model and reduce its complexity.
Overall, regularization is an essential tool in machine learning that can help improve the performance and reliability of models, and it is an important technique for practitioners to be familiar with.
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4:21 AM
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