Overfitting occurs whenever a model attempts to predict a trend from noisy data. This is the result of a model with an excessive number of parameters. A model that has been overfitted is misleading because the tendency does not correspond with the data’s actuality. This can be determined if the model performs well on the observed data (training set) but poorly on the unseen data (test set). The objective of an ML model is to properly categorize training data to any problem domain data. This is crucial – we want our model to predict future outcomes based on data it has never previously seen.
- Data augmentation techniques are used to prevent overfitting.
For neural networks, data augmentation is the simple process of expanding the quantity of the data to increase the number of pictures existing in the dataset. Popular picture enhancement methods include mirroring, translation, rotation, scaling, adjusting brightness, and adding noise.
The first step in addressing overfitting is to reduce the model’s complexity. To lower it, we simply eliminate layers or decrease the number of neurons. It is essential to determine the input and output parameters of the different layers of the neural network throughout this process. There is no clear guideline for how much to eliminate or the optimal size of a network, but if your neural network is overfitting, try shrinking it.
Early halting is a method of regularization used when training a model using an iterative approach (such as gradient descent). This strategy is applicable to all problems, given that all neural networks learn only by gradient descent. With each iteration, this technique modifies the model to better suit the training data. This enhances the model’s performance on test set data up to a certain degree. However, at a certain threshold, enhancing the model’s fit to the training data results in an increase in generalization error. The early stopping criteria specify how many iterations may be performed before the model becomes overfit.