title: How to plot Lime report when there is a lot of features in data-set
body: <p>I'm trying to plot lime report classification algorithm using the method <code>as_pyplot_figure()</code> for the explanation from LimeTabularExplainer. It is working but the data which i'm saving locally in html format using <code>save_html()</code> from mpld3 library which is coming as too compressed ( no visible actually).
  Any other way to handle this scenario, will be helpful.</p>
  <p>My code currently looks like</p>
  <pre><code> from lime.lime_tabular import LimeTabularExplainer
      model= LGBMClassifier(boosting_type='gbdt', class_weight=None, 
      colsample_bytree=1.0,
      importance_type='split', learning_rate=0.1, max_depth=-1,
      min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
      n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
      random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,
      subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
  
      predict_function = model.predict
  
      explainer = LimeTabularExplainer(train_data,mode='classification')
      exp = explainer.explain_instance(
                      data, predict_function)
      fig = exp.as_pyplot_figure()
      
      mpld3.save_html(fig, lime_report.html)
  </code></pre>