title: Most important features in MLPClassifier in Sklearn
body: <p>I would like to know if there is any way to visualize or find the most important/contributing features after fitting a MLP classifier in Sklearn.</p>
  
  <p>Simple example:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import LeaveOneOut
  from sklearn.neural_network import MLPClassifier
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  
  
  data= pd.read_csv('All.csv', header=None)
  X, y = data.iloc[0:, 0:249].values, data.iloc[0:,249].values
  
  sc = StandardScaler()
  mlc = MLPClassifier(activation = 'relu', random_state=1,nesterovs_momentum=True)
  loo = LeaveOneOut()
  pipe = make_pipeline(sc, mlc)
  
  parameters = {"mlpclassifier__hidden_layer_sizes":[(168,),(126,),(498,),(166,)],"mlpclassifier__solver" : ('sgd','adam'), "mlpclassifier__alpha": [0.001,0.0001],"mlpclassifier__learning_rate_init":[0.005,0.001] }
  clf = GridSearchCV(pipe, parameters,n_jobs= -1,cv = loo)
  clf.fit(X, y)
  
  model = clf.best_estimator_
  print("the best model and parameters are the following: {} ".format(model))
  </code></pre>