title: scikit overfitting all the time
body: <p><code>Scikit</code> is overfitting when I am using it for machine learning. For example, I am using <code>decisiontree</code> for a regression. </p>
  
  <p>The training set gave me 0.9998 for the <code>r_value</code>;
  the test set gave me 0.3134 for the <code>r_value</code>;
  after cross-validation, the test set gave me 0.1695 for the <code>r_value</code>.</p>
  
  <p>And if I use <code>cross_val_predict</code> function, will I have to add the test set target value? Or will it give me an error? I am wondering how to avoid inputting test set target value?  </p>
  
  <pre><code>dt=tree.DecisionTreeRegressor().fit(feature,target)
  
  slope,intercept,r_value,p_value,std_err=stats.linregress(target,dt.predict(feature))
  print r_value
  slope,intercept,r_value,p_value,std_err=stats.linregress(target_test,dt.predict(feature_test))
  print r_value
  
  predicted=cross_validation.cross_val_predict(dt,     feature_test,target_test,cv=5)
  slope,intercept,r_value,p_value,std_err=stats.linregress(target_test,predicted)
  print r_value
  </code></pre>