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Regularization techniques help prevent overfitting by adding a penalty to large coefficients from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Regularization techniques are a useful tool in preventing overfitting, a common challenge in predictive modeling. Overfitting occurs when a model learns the training data too well, capturing noise and randomness instead of the underlying patterns. This can lead to poor performance on new, unseen data. To address overfitting, regularization techniques introduce a penalty term to the model's cost function that discourages overly complex models. One popular form of regularization is L2 regularization, also known as ridge regression, which penalizes large coefficients by adding their squared values to the cost function. By doing so, the model is encouraged to prioritize simpler solutions with smaller coefficients, reducing the risk of overfitting. Another common regularization technique is L1 regularization, or lasso regression, which penalizes large coefficients by adding their absolute values to the cost function. This encourages sparsity in the model, as it tends to push some coefficients to zero, effectively selecting only the most important features. Regularization techniques strike a balance between fitting the training data well and generalizing to new data by controlling the complexity of the model. By penalizing large coefficients, regularization helps prevent overfitting and improves the model's performance on unseen data. These techniques are essential tools in the data scientist's toolbox for building robust and reliable predictive models.
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