What is the rationale for discretization of continuous features and when should it be done? Continous feature discretization usually leads to lose of information due to the binning process. However most of the Top solutions for Kaggle Titanic are based on discretization(age,fare).

When should continuous features be discretized ? Is there any criteria and pros and cons on accuracy.
One reason to discretize continuous features is to improve signal-to-noise ratio.  Fitting a model to bins reduces the impact that small fluctuates in the data has on the model, often small fluctuates are just noise. Each bin "smooths" out the fluctuates/noises in sections of the data.