How do you handle overfitting and underfitting in neural network competitions?

In a general sense, data contains both information and noise. When a model neither captures information nor noise it is considered underfit. In the case of overfitting the model captures both information and noise, hence a good model is expected to capture information only. A model is considered overfitting when it performs well on the training set and poorly on the test set. To address this problem, a regularization method is used to calibrate the model to adjust the loss function to avoid overfitting and underfitting.

In my opinion especially for CNN models, over-fitting represents the central issue associated with obtaining well-behaved generalization(the ability of the model to adapt properly to new or previously unseen data). Overfitting : We can say that a model is over-fitted in a cases where the model performs well on training data and does not succeed on the test data(unseen data). Underfitting : An underfitted model is just the opposite; this case occurs when the model learn sufficient amount from the training data. hence it also cant perform well when encountered with previously unseen data. Then there's our just-fitted/balanced model, this dude is really what we want for a good generalization purpose.

Underfitting occurs in a machine learning algorithm when the model is overly simplistic and fails to grasp the intricacies present in the data. This results in poor performance on both the training and testing datasets, making the model inaccurate, especially when dealing with new, previously unseen examples. Underfitting often arises when an excessively basic model is employed with overly simplified assumptions. To mitigate the issue of underfitting, it is advisable to utilize more complex models with improved feature representations and reduce the degree of regularization.

It is generally accepted that overfitting happens when a neural network is changed to the point that it captures unimportant details and noise that makes it less dexterous when it comes to more data. On the contrary, underfitting is a condition where the network is unable to gain the necessary knowledge from the training data, which means that it performs badly on both training and validation datasets. The two overfitting and underfitting being the main problem that can stand in the way of the model is the strongest weapon to help you.

There are some red flags to spot overfitting and underfitting, for example if model performs much better on training set vs the testing set then it is probably overfitting. On the other hand, if error rate is high on both training and testing sets then it is probably underfitting. When there is underfitting, consider increasing the size of the neural networks model, when there is overfittin, consider using more data.

I'm suggesting you one of the method i found to be helpful while detecting the over/under fitted models and that is : --> Learning Curves : Its really a game changer, like plot the learning curves using the help of matplot showing the model's performance for example plot the accuracy or loss on both training and validation. -> A large gap between two curves shows overfitting. -> If the learning curve for both training and validation sets may appear relatively flat. indicating the model isn't learning from the data. and i have faced under fitting while training my model and let me tell you this, That feeling kinda hurts :( so please make sure you are taking the necessary steps to make your model an Optimal one :)

Overfitting occurs when a model is too complex and starts to fit the noise in the training data rather than the underlying patterns. Here's a brief summary of your suggested approaches: 1-Increase Training Data: A larger dataset can help the model generalize better, as it has more diverse examples to learn from. 2-Reduce Model Complexity: Simplifying the model architecture by reducing the number of parameters or layers can make it less prone to overfitting. 3-Early Stopping: Monitoring the loss during training and stopping when it starts to increase on the validation dataset helps prevent overfitting and ensures the model is not overly trained. 4-Regularization Techniques

So to prevent over fitting there are some many methods out there but let me tell you the easiest one and that's the iconic Data Augmentation -------------------------- Data Augmentation is the easiest method to prevent our model from getting affected with the so called Overfitting. Training the model on a sizeable amount of data is the easiest way to avoid over-fitting and to achieve this, Data augmentation is used.

1. Increase the number of parameters in the neural network either by adding more hidden layers or adding more neurons to a layer. 2. See that the learning rate is not too low which prevents the model from updating the weights. 3. Perform feature engineering to add meaningful features that help the model in learning the signals even better. 4. Train the model for a longer time by increasing the number of epochs. 5. See that the input features have some kind of relationship with the target. Don't just try to predict the age of a person given his name.

To fix underfitting the method i have found to be useful is 'Train for more Epoch'. underfitting may occur if the model is not been trained for a sufficient number of epoch so try increasing the number of training epoch to allow the model more time to learn from the data. [ Epoch : The one complete pass through the entire training dataset during the training of a neural network. ]

you can do many things to enhance your neural network such as: 1-Increase number of hidden Layers 2-Increase number of neurons 3-normalizing or scaling your data 4- increase your data