title: Why the sum of all the permutations (perm, feature_importances_) are not equal to one?
body: <p>To calculate the feature importance for Keras model I used the following code:</p>
  <pre><code>from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
  import eli5
  from eli5.sklearn import PermutationImportance
  
  perm = PermutationImportance(model, random_state=1,scoring='r2').fit(X_train,y_train)
  eli5.show_weights(perm, feature_names = ['a', 'b','c','d','e','f','g','h','i','j'])
  </code></pre>
  <p>The result I got is:</p>
  <pre><code>Weight            Feature
  1.4285 ± 0.1245      i
  1.3773 ± 0.1232      f
  1.3723 ± 0.1445      d
  1.3641 ± 0.2071      h
  1.3155 ± 0.1234      c
  1.2414 ± 0.1130      e
  1.1526 ± 0.1501      j
  1.0985 ± 0.0817      g
  0.8267 ± 0.0274      a
  0.7618 ± 0.0886      b
  </code></pre>
  <p>My doubt is that, the feature importance is calculated as a probability. But here the first value itself is greater than 1. Why so?</p>