title: Python equivalent for R StepAIC for Logistic Regression (direction='Backwards')
body: <p>I tried to emulate stepAIC function in R doing it &quot;manually&quot; but it takes forever (I posted just the first two tries). Is there something similar to stepAIC function (that eliminates one variable with highest p-value at iteration and minimize AIC) in python for logistic regression?</p>
  <pre><code>#create model with double interactions
  datapol = data.drop(['flag'], axis=1) #elimino colonna flag dai dati
  poly=sklearn.preprocessing.PolynomialFeatures(interaction_only=True,include_bias = False)
  
  #calculate AIC for model with double interactions 
  m_sat=poly.fit_transform(datapol)
  m1=sm.Logit(np.asarray(flag.astype(int)),m_sat.astype(int))
  m1.fit()
  print(m1.fit().summary2())
  
  #create new model without variable that has p-value&gt;0.05
  mx1=pd.DataFrame(m_sat)
  mx2=np.asarray(mx1.drop(mx1.columns[[3]], axis=1))
  m2=sm.Logit(np.asarray(flag.astype(int)),mx2.astype(int))
  m2.fit()
  print(m2.fit().summary2())
  </code></pre>
  <p>edit: I found an algorithm that emulate stepAIC using forward direction <a href="https://qiita.com/mytk0u0/items/aa2e3f5a66fe9e2895fa" rel="nofollow noreferrer">https://qiita.com/mytk0u0/items/aa2e3f5a66fe9e2895fa</a></p>