title: Regularization parameter setting for Randomized Regression in sklearn
body: <p>I am using sklearn Randomized Regression, such as <a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RandomizedLogisticRegression.html" rel="nofollow noreferrer">Randomized Logistic Regression</a>. Because randomized logistic regression uses L1-penalty, it is require to set the regularization parameter C(or alpha in Lasso).</p>
  
  <p>To find good value for C, I usually used simple GridSearchCV like below.</p>
  
  <p>But RandomizedLogisticRegression() does not support GridSearchCV, because it contains the bootstrapping. Instead, I tried to use typical LogisticRegression with GridSearchCV.</p>
  
  <pre><code>params = {'C':[0.1, 1, 10]}
  logi = LogisticRegression(penalty='l1')
  clf = GridSearchCV(logi, params, cv=10)
  </code></pre>
  
  <p>I could get C by this way, however, no attribute was selected when I apply this C value to Randomized logistic regression. Maybe the selected C by GridSearchCV was quite low.</p>
  
  <p>So, I would like to know that whether there are any other good way for determining the fair value of C(or alpha), when using Randomized regression.</p>
  
  <p>There was a <a href="https://stackoverflow.com/questions/33810051/how-do-i-found-the-lowest-regularization-parameter-c-using-randomized-logistic">similar question</a> before, but I think that answer was for typical regression.</p>
  
  <p>Can anyone give me an idea please?</p>