title: Which parameters within the sklearn.decomposition.LatentDirichletAllocation will improve the performance of the Topic Modelling?
body: <p>I am currently trying to optimise a LDA model I am doing on some text data. Beyond changing the number of topics, what other parameters will significantly improve the model?</p>
  <p>This is my current code which I copied from another <a href="https://stackoverflow.com/questions/61373994/how-to-specify-random-state-in-lda-model-for-topic-modelling">stackoverflow Q&amp;A</a>:</p>
  <pre><code>LatentDirichletAllocation(n_components=10,        
                                    max_iter=10,               
                                    learning_method='online',   
                                    random_state=100,          
                                    batch_size=128,            
                                    evaluate_every = -1,       
                                    n_jobs = -1 )
  </code></pre>
  <p>I am having trouble understanding what these different parameters actually do. The <a href="https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html" rel="nofollow noreferrer">sklearn explanation from the official site lacks detail</a> and every <a href="https://medium.com/@yanlinc/how-to-build-a-lda-topic-model-using-from-text-601cdcbfd3a6" rel="nofollow noreferrer">medium article</a> skims over what these parameters do. Is there a source that breaks down these in plain english?:</p>
  <ul>
  <li>learning_method</li>
  <li>evaluate _every</li>
  <li>n_jobs = -1</li>
  <li>learning_offset=10.0,</li>
  <li>ax_doc_update_iter=100</li>
  <li>max_iter=10,</li>
  <li>n_jobs=None,</li>
  <li>random_state=42,</li>
  <li>topic_word_prior=None</li>
  </ul>
  <p>I'm aware I don't have a statistics background but I want to understand the code I'm implementing a bit more. Thanks</p>