title: How to get odds-ratios and other related features with scikit-learn
body: <p>I'm going through this <a href="https://stats.idre.ucla.edu/stata/faq/how-do-i-interpret-odds-ratios-in-logistic-regression/" rel="nofollow noreferrer">odds ratios in logistic regression tutorial</a>, and trying to get the exactly the same results with the logistic regression module of scikit-learn. With the code below, I am able to get the coefficient and intercept but I could not find a way to find other properties of the model listed in the tutorial such as <em>log-likelyhood, Odds Ratio, Std. Err., z, P&gt;|z|, [95% Conf. Interval]</em>. If someone could show me how to have them calculated with <code>sklearn</code> package, I would appreciate it.</p>
  <pre><code>import pandas as pd
  from sklearn.linear_model import LogisticRegression
  
  url = 'https://stats.idre.ucla.edu/wp-content/uploads/2016/02/sample.csv'
  df = pd.read_csv(url, na_values=[''])
  y = df.hon.values
  X = df.math.values
  y = y.reshape(200,1)
  X = X.reshape(200,1)
  clf = LogisticRegression(C=1e5)
  clf.fit(X,y)
  clf.coef_
  clf.intercept_
  </code></pre>