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Generalization: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
Generalization in machine learning refers to how accurately can a trained model correctly predict new, unseen data. A model that generalizes well is the opposite of one that overfits on [[training data]]. It's an essential concept in machine learning since it allows models to be applied in real-world problems where input data may change frequently. | |||
Generalization in machine learning refers to | |||
Machine learning models are trained by optimizing their parameters to minimize the difference between predictions and actual outcomes in training data. If the model becomes overfitted to this training data, it may become complex and unable to generalize well to new information. Overfitting occurs when the model fits noise rather than underlying patterns in the training data; consequently, it becomes too specialized for new data sets and performs poorly when given new ones. | Machine learning models are trained by optimizing their parameters to minimize the difference between predictions and actual outcomes in training data. If the model becomes overfitted to this training data, it may become complex and unable to generalize well to new information. Overfitting occurs when the model fits noise rather than underlying patterns in the training data; consequently, it becomes too specialized for new data sets and performs poorly when given new ones. | ||
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In this section, we will look at some common techniques for improving the generalization performance of machine learning models. | In this section, we will look at some common techniques for improving the generalization performance of machine learning models. | ||
===Regularization== | ===Regularization=== | ||
Regularization is a technique that adds a penalty term to an objective function during training, discouraging models from becoming too complex. This penalty can be based on either the magnitude of weights in the model or on its number of non-zero weights. Regularization helps prevent overfitting by forcing the model to prioritize simpler solutions which perform better across different situations. | Regularization is a technique that adds a penalty term to an objective function during training, discouraging models from becoming too complex. This penalty can be based on either the magnitude of weights in the model or on its number of non-zero weights. Regularization helps prevent overfitting by forcing the model to prioritize simpler solutions which perform better across different situations. | ||
Two common types of regularization are L1 regularization and L2 regularization. L1 adds a penalty term proportional to the absolute value of the weights, while L2 applies one based on the square root of those same weights - also referred to as weight decay. | Two common types of regularization are L1 regularization and L2 regularization. L1 adds a penalty term proportional to the absolute value of the weights, while L2 applies one based on the square root of those same weights - also referred to as weight decay. | ||
===Early Stopping== | ===Early Stopping=== | ||
Early stopping is a technique that involves monitoring the validation loss during training and stopping the process when it stops improving. This prevents overfitting by terminating the model before it becomes too specialized for your training data. | Early stopping is a technique that involves monitoring the validation loss during training and stopping the process when it stops improving. This prevents overfitting by terminating the model before it becomes too specialized for your training data. | ||
===Data Augmentation== | ===Data Augmentation=== | ||
Data enhancement is a practice that involves creating new information. | Data enhancement is a practice that involves creating new information. | ||