Estimating Prediction Error

Estimating Prediction error predicts how well model works on unseen data. The Estimation prediction error can be estimated in various ways and among the various methods or techniques are Cross validation ,validation set and bootstrap.

In validation set comparison we use the model to predict values with dependent variable for validation set. Then we compare predicted values with the actual value of dependent variable to calculate the prediction error.

In the cross validation we iteratively split the data into subsamples, create a new training set and validation set. Then evaluating the model with the validation set.

The prediction error is due to the overfitting data where the model is trained well with fluctuations and noise training data. So by taking the large datasets overfitting can be prevented.