title: Sklearn SVM: SVR and SVC, getting the same prediction for every input
body: <p><strong>Here is a paste of the code: <a href="http://pastebin.com/dvWy5SpE" rel="noreferrer">SVM sample code</a></strong></p>
  
  <p>I checked out a couple of the other answers to this problem...and it seems like this specific iteration of the problem is a bit different.  </p>
  
  <p>First off, my inputs are normalized, and I have five inputs per point.  The values are all reasonably sized (healthy 0.5s and 0.7s etc--few near zero or near 1 numbers). </p>
  
  <p>I have about 70 x inputs corresponding to their 70 y inputs.  The y inputs are also normalized (they are percentage changes of my function after each time-step). </p>
  
  <p>I initialize my SVR (and SVC), train them, and then test them with 30 out-of-sample inputs...and get the exact same prediction for every input (and the inputs are changing by reasonable amounts--0.3, 0.6, 0.5, etc.).   I would think that the classifier (at least) would have some differentiation...</p>
  
  <p>Here is the code I've got: </p>
  
  <pre><code># train svr
  
  my_svr = svm.SVR()
  my_svr.fit(x_training,y_trainr)
  
  # train svc
  
  my_svc = svm.SVC()
  my_svc.fit(x_training,y_trainc)
  
  
  # predict regression
  
  p_regression = my_svr.predict(x_test)
  p_r_series = pd.Series(index=y_testing.index,data=p_regression)
  
  # predict classification
  
  p_classification = my_svc.predict(x_test)
  p_c_series = pd.Series(index=y_testing_classification.index,data=p_classification)
  </code></pre>
  
  <p>And here are samples of my inputs:</p>
  
  <pre><code>x_training = [[  1.52068627e-04   8.66880301e-01   5.08504362e-01   9.48082047e-01
  7.01156322e-01],
                [  6.68130520e-01   9.07506250e-01   5.07182647e-01   8.11290634e-01
  6.67756208e-01],
                ... x 70 ]
  
  y_trainr = [-0.00723209 -0.01788079  0.00741741 -0.00200805 -0.00737761  0.00202704 ...]
  
  y_trainc = [ 0.  0.  1.  0.  0.  1.  1.  0. ...]
  </code></pre>
  
  <p>And the <code>x_test</code> matrix (5x30) is similar to the <code>x_training</code> matrix in terms of magnitudes and variance of inputs...same for <code>y_testr</code> and <code>y_testc</code>.</p>
  
  <p>Currently, the predictions for all of the tests are exactly the same (0.00596 for the regression, and 1 for the classification...)</p>
  
  <p>How do I get the SVR and SVC functions to spit out relevant predictions?  Or at least different predictions based on the inputs... </p>
  
  <p>At the very least, the classifier should be able to make choices.  I mean, even if I haven't provided enough dimensions for regression...</p>