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Overfitting must be avoided to ensure model generalization from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Overfitting occurs when a model learns the training data too well, to the point that it starts to memorize the data rather than generalize from it. In other words, the model becomes too complex and starts to capture noise in the training data, rather than the underlying patterns. This can lead to poor performance when the model is applied to new, unseen data. To ensure that a model generalizes well to new data, it's important to avoid overfitting. One way to prevent overfitting is by using regularization techniques, which add a penalty to the model's complexity, discouraging it from fitting the noise in the training data. Another approach is to use cross-validation, where the data is split into training and validation sets multiple times to evaluate the model's performance on different subsets of the data. Feature selection is another strategy to prevent overfitting, by reducing the number of input variables to only those that are most relevant for predicting the target variable. This helps simplify the model and reduce the risk of overfitting. Additionally, ensembling techniques, such as bagging and boosting, can help improve the generalization of a model by combining multiple weak learners to create a stronger, more robust model.- The goal of any machine learning model is to generalize well to new, unseen data. By avoiding overfitting and focusing on techniques that promote generalization, you can improve the performance and reliability of your model in real-world applications.
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