sketching our roadmap we will build system using real data that is very noisy this is not for the faintheartedas we will not arrive at the golden solution for classifier that achieves percent accuracy this is because even humans often disagree whether an answer was good or not (just look at some of the comments on the stackoverflow com websitequite the contrarywe will find out that some problems like this one are so hard that we have to adjust our initial goals on the way but on that waywe will start with the nearest neighbor approachfind out why it is not very good for the taskswitch over to logistic regressionand arrive at solution that will achieve good prediction quality but on smaller part of the answers finallywe will spend some time on how to extract the winner to deploy it on the target system learning to classify classy answers while classifyingwe want to find the corresponding classessometimes also called labelsfor the given data instances to be able to achieve thiswe need to answer the following two questionshow should we represent the data instanceswhich model or structure should our classifier possesstuning the instance in its simplest formin our casethe data instance is the text of the answer and the label is binary value indicating whether the asker accepted this text as an answer or not raw texthoweveris very inconvenient representation to process for most of the machine learning algorithms they want numbers it will be our task to extract useful features from raw textwhich the machine learning algorithm can then use to learn the right label tuning the classifier once we have found or collected enough (text and labelpairswe can train classifier for the underlying structure of the classifierwe have wide range of possibilitieseach of them having advantages and drawbacks just to name some of the more prominent choicesthere is logistic regressionand there are decision treessvmsand naive bayes in this we will contrast the instance-based method from the previous with model-based logistic regression