title: How to fit a model to my testing set in statsmodels (python)
body: <p>I am working on a logistic regression model and I am having trouble understanding how to take the model fit from my training set onto my testing set.  Sorry, I am new to python and VERY new to statsmodels..</p>
  
  <pre><code>import pandas as pd
  import statsmodels.api as sm
  from sklearn import cross_validation
  
  independent_vars = phy_train.columns[3:]
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(phy_train[independent_vars], phy_train['target'], test_size=0.3, random_state=0)
  X_train = pd.DataFrame(X_train)
  X_train.columns = independent_vars
  X_test = pd.DataFrame(X_test)
  X_test.columns = independent_vars
  y_train = pd.DataFrame(y_train)
  y_train.columns = ['target']
  y_test = pd.DataFrame(y_test)
  y_test.columns = ['target']
  logit = sm.Logit(y_train,X_train[subset],missing='drop')
  result = logit.fit()
  print result.summary()
  
  y_pred = logit.predict(X_test[subset])
  </code></pre>
  
  <p>From the last line, I get this error:</p>
  
  <blockquote>
    <blockquote>
      <blockquote>
        <p>y_pred = logit.predict(X_test[subset])
            Traceback (most recent call last):
              File "", line 1, in 
              File "C:\Users\eMachine\WinPython-64bit-2.7.5.3\python-2.7.5.amd64\lib\site-packages\statsmodels\discrete\discrete_model.py", line 378, in predict
                return self.cdf(np.dot(exog, params))
            ValueError: matrices are not aligned</p>
      </blockquote>
    </blockquote>
  </blockquote>
  
  <p>My training and testing data set have the same number of variables so I am sure I am misunderstanding what the logit.predict() is actually doing.</p>