title: Why MNLogit returns `classes_num - 1` params and how get them all?
body: <p>If I have few classes, i.e. 3. I am expecting to get 3 Generalized Linear Regression coefficients arrays, as in <code>sklearn.linear_model.LogisticRegression</code> but <code>statsmodels.discrete.discrete_model.MNLogit</code> provides classes_num - 1 coefficients (in this case -- 2).</p>
  
  <p>Example:</p>
  
  <pre class="lang-py prettyprint-override"><code>import statsmodels.api as st
  from sklearn.linear_model import LogisticRegression
  
  
  iris = st.datasets.get_rdataset('iris','datasets')
  
  y = iris.data.Species
  x = iris.data.iloc[:, :-1]
  mdl = st.MNLogit(y, x)
  # mdl_fit = mdl.fit()
  mdl_fit = mdl.fit(method='bfgs' , maxiter=1000)
  print(mdl_fit.params.shape)  # (4, 2)
  
  model = LogisticRegression(fit_intercept = False, C = 1e9)
  mdl = model.fit(x, y)
  print(model.coef_.shape)  # (3, 4)
  </code></pre>
  
  <p>How I am supposed to get regression coefficients for all 3 classes using MNLogit?</p>