title: Making predictions with logistic regression (Python Sci Kit Learn)
body: <p>I am relatively new to <a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow noreferrer">logistic regression</a> using <a href="http://scikit-learn.org/" rel="nofollow noreferrer">SciKit learn</a> in Python. After reading some topics and viewing some demo's, I decided to dive in myself. </p>
  
  <p>So, basically, I am trying to predict the conversion rate of customers, based on some features. The outcome is either <em>Active</em> (1) or <em>Not active</em> (0). I tried <em>KNN</em> and <em>logistic regression</em>. With <em>KNN</em> I get an average accuracy of <code>0.893</code> and with logistic regression <code>0.994</code>. The latter seems so high, is that even realistic / possible?</p>
  
  <p>Anyway: Suppose that my model is indeed very accurate, I would now like to import a new dataset with the same feauture columns and predict their conversions (they end this month). In the case above I used <code>cross_val_score</code> to get the accuracy scores.</p>
  
  <p>Do I now need to import the new set, somehow fit that new set to this model. (not training it again, now I just want to use it)</p>
  
  <p>Can someone please inform me how I can proceed? If additional info is needed, please comment on that.</p>
  
  <p>Thanks in advance! </p>