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Regularization techniques are used to prevent overfitting in machine learning models. Overfitting occurs when a model learns the training data too closely and fails to make accurate predictions on new data. Regularization helps by imposing penalties for complexity, making the model simpler and more generalizable.
One common regularization technique is L1 regularization, also known as Lasso regularization, which penalizes the sum of the absolute values of the weights in the model. This technique can drive some coefficients to zero, effectively performing feature selection.
Another technique is L2 regularization, also known as Ridge regularization, which penalizes the sum of the squares of the weights. This technique helps to reduce overfitting by constraining the magnitude of the coefficients.
Dropout regularization randomly drops out a subset of neurons during training, forcing the network to learn more robust features. This technique prevents the model from becoming overly reliant on any single neuron, reducing the risk of overfitting.
Data augmentation is a technique that involves creating new training examples by applying random transformations to existing data. This technique increases the diversity of the training dataset, helping the model generalize better to new, unseen data.
Early stopping is a technique that monitors the model’s performance on a validation dataset during training and stops the training process when the performance starts to degrade. This technique prevents the model from overfitting to the training data.
While these regularization techniques are effective in preventing overfitting, they also have drawbacks such as increased computational burden, the need for domain-specific knowledge, and the risk of premature stopping. Careful tuning of parameters is required to ensure optimal performance.
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Source link: https://medium.com/@mateoquirosaspron/regularization-the-art-of-simplifying-models-eca4fb7dbd59?source=rss——ai-5
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