image source: mlexplained Overfittng happens in every machine learning (ML) problem. Learning how to deal with overfitting is essential to mastering machine learning. The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to the process of adjusting a model to get the best performance possible on the training data (the learning in machine learning ), whereas generalization refers to how well the trained model performs on data it has never seen before . The goal of the game is to get good generalization, of course, but you don’t control generalization; you can only adjust the model based on its training data. The processing of fighting overfitting is a way called regularization . [1]. How do you know whether a model is overfitting? The best initial method is to measure error on a training and test set. If you see a low error on the training set and...
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