How can you understand under fitting and over fitting in Neural Network?

Ans: A model is said to have underfitting when it cannot capture the underlying trend of the data. Underfitting destroys the accuracy of model. Its occurrence simply means that our model not fit the data well enough. It usually happens when we have less data to build an accurate model and also when we try to build a linear model with a non-linear data. Underfitting can be avoided by using more data and also reducing the features by feature selection.

Overfitting:
A model is said to be overfitted, when we train it with a lot of data. When a model gets trained with so much of data, it starts learning from the noise and inaccurate data entries in our data set. Then the model does not categorize the data correctly, because of too much of details and noise. The causes of overfitting are the non-parametric and non-linear methods because these types of models have more freedom in building the model based on the dataset and therefore, they can really build unrealistic models.