title: Interpreting logistic regression feature coefficient values in sklearn
body: <p>I have fit a logistic regression model to my data. Imagine, I have four features: 1) which condition the participant received, 2) whether the participant had any prior knowledge/background about the phenomenon tested (binary response in post-experimental questionnaire), 3) time spent on the experimental task, and 4) participant age. I am trying to predict whether participants ultimately chose option A or option B. My logistic regression outputs the following feature coefficients with  <code>clf.coef_</code>:</p>
  
  <pre><code>[[-0.68120795 -0.19073737 -2.50511774  0.14956844]]
  </code></pre>
  
  <p>If option A is my positive class, does this output mean that feature 3 is the most important feature for binary classification and has a negative relationship with participants choosing option A (note: I have not normalized/re-scaled my data)? I want to ensure that my understanding of the coefficients, and the information I can extract from them, is correct so I don't make any generalizations or false assumptions in my analysis. </p>
  
  <p>Thanks for your help!</p>