title: Python : How to interpret the result of logistic regression by sm.Logit
body: <p>When I run a logistic regression using <a href="https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Logit.html" rel="nofollow noreferrer"><code>sm.Logit</code></a> (from the <a href="https://www.statsmodels.org/stable/index.html" rel="nofollow noreferrer"><code>statsmodel</code></a> library), part of the result looks like this:</p>
  <pre><code>Pseudo R-squ.:                  0.4335
  
  Log-Likelihood:                -291.08
  
  LL-Null:                       -513.87
  
  LLR p-value:                 2.978e-96
  </code></pre>
  <p>How could I explain the significance of the model? Or say, the ability of explaining? Which indicator should I use? I have searched online and there isn't much information about <code>Pseudo R2</code> and <code>LLR pvalue</code>. I'm confused and I don't know how to judge the performance of my model based on these numbers</p>