The concept of overfitting is a modeling error that appears when a function is nearly united to a limited data set point in statistics; hence the model is essential in reference only to its original data set and not any other sets of data. Overfitting the model usually creates an excessively composite model to describe eccentricities in the data under study. Data that is often studied in reality has random noise within it or some notch of error in it; hence trying to create the model imitates too closely somewhat inaccurate data, which can infect the model with extensive errors and decrease its analytical power.
Methods of Avoiding Overfitting
Simplifying the model
The first step when operating with an overfitting model is to minimize its complexity. Decreasing the complexity can be done by simply removing layers or reducing the number of neurons to make the network lesser. It is significant to calculate the output and input dimensions of the several layers involved in the neural network during the model simplification. There is a usual rule that guides how extensive the network should be or how much one should remove, though when the neural network overfits, it’s good to try reducing it.
Early stopping is a type of regularization when training an overfitting model with an iterative method like gradient descent. Early stoppings can be a technique for overfitting problems because all the neural networks learn exclusively by using gradient descent. The early stopping helps update the model to make it better suit the training data with every iteration. Through doing that, it improves the performance of the model on data on the test set. Early stoppings guidelines guide how many reiterations can be run before the model starts to overfit.