title: Predictive Analytics - "why" factor & model interpretability
body: <p>I have the data that contains tons of x variables that are mainly categorical/nominal and my target variable is a multi-class label. I am able to build a couple models around to predict multi-class variables and compare how each of them performed. I have training and testing data. Both the training and testing data gave me good results.</p>
  <p>Now, I am trying to find out &quot;why&quot; did the model predicted certain Y-variable? Meaning if I have weather data: X Variable: city, state, zip code, temp, year; Y Variable: rain, sun, cloudy, snow. I want to find out &quot;why&quot; did the model predict: rain, sun, cloudy, or snow respectfully. I used classification algorithms like multi-nominal, decision tree, ... etc</p>
  <p>This may be a broad question but I need somewhere I can start researching. I can predict &quot;what&quot; but I can't see &quot;why&quot; it was predicted as rain, sun, cloudy, or snow label. Basically, I am trying to find the links between the variables that caused to predict the variable.</p>
  <p>So far I thought of using correlation matrix, principal component analysis (that happened during model building process)...at least to see which are good predictors and which ones are not. Is there is a way to figure out &quot;why&quot; factor?</p>