Overfitting and Underfitting

Overfitting is a fundamental problem in statistics and machine learning where 

the model is overly adapted to a particular collection of data 

(e.g. the collection available for training) and thus less successful in forecasting.

One of the reasons that can cause Overfitting is when the model has more parameters

then it shpould.The excess parameter allows the model to study the statistical noise

as if it represents real behavior.

How to Prevent Overfitting?

Use techniques like: Cross-validation,Remove features,Regularization,Train with more data 

Underfitting, on the other hand, occurs when the statistical model is too simple 

to properly represent the basic structure of the data,

for example due to a minority of parameters that define the model. 

An example is an attempt to use a linear model to describe nonlinear behavior.