Show pageOld revisionsBacklinksBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== Machine Learning - Preventing Overfitting ====== * **Early Stopping**: In this method, we track the loss on the validation set during the training phase and use it to determine when to stop training such that the model is accurate but not overfitting. * **Image Augmentation**: Artificially boosting the number of images in our training set by applying random image transformations to the existing images in the training set. * **Dropout**: Removing a random selection of a fixed number of neurons in a neural network during training. However, these are not the only techniques available to prevent overfitting. You can read more about these and other techniques in the link below: [[https://hackernoon.com/memorizing-is-not-learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42|Memorizing is not learning! — 6 tricks to prevent overfitting in machine learning]] CKG Edit computer_science/machine_learning/preventing_overfitting.txt Last modified: 2023/12/01 12:07by 127.0.0.1