title: R predictive model reason for predicitions and propensity %
body: <p>very new to R and machine learning however I'm having to undertake a project to predict customer churn based on a number of variables e.e. length of service, number of credit notes issued, number of missed deliveries, number of price increases etc.</p>
  
  <p>I'm using rpart and randomforest and have got a dataset with a churn prediction against each one.  I am able to produce a confidence matrix and to see which are the important indicators.  However, the aim with the output is to send to the Sales team as an 'at risk' list of customers to deal with.  </p>
  
  <p>What would be really important for this is one to append the confidence/propensity/liklihood % to churn so I can rank in order of risk but also, is there a way to append a category/summary/reason for each customer as to why they were predicted to churn - i.e. customer abc - high score on price increases so we need to be careful with pricing,. customer def - high on missed deliveries - need to fix our service?</p>
  
  <p>Many thanks for your help.</p>