title: Categorical dependent variable in Multinomial Logistic Regression
body: <p>I am using <code>scikit-learn LogisticRegression</code> on a dataset where the dependent variable is a categorical variable with 10 possible values (labelled 1 to 10). My statistical knowledge is fairly rudiemntatry.</p>
  
  <p>The probabilities of each output value are inherently constrained by the requirement that they must sum to <code>1</code>, and my understanding of the maths is that there needs to be a <code>reference category</code> (e.g. the most likely category) and that the probability of each of the other 9 outcomes is then expressed relative to the probability of the reference category, so effectively there are 9 coefficients (rather than 10) for each explanatory variable.</p>
  
  <p>I'm not clear whether this is done automatically by the <code>LogisticRegression.fit()</code> method, and if so does it assume the first category is the reference (<code>1</code>)? 
  or do I need to somehow process the input data so there are only 9 categories?
  or do I ignore this and use the 10 coefficients as they are output?</p>