title: Specify Huber loss epsilon parameter in sklearn GradientBoostingRegressor
body: <p>I am using <code>sklearn.ensemble.GradientBoostingRegressor</code> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html" rel="nofollow noreferrer">link</a> and one of the options is to set the loss function to <code>Huber</code>.
  The <code>Huber</code> method itself <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html" rel="nofollow noreferrer">link</a>, has a parameter <code>epsilon</code> to specify the robustness to outliers.</p>
  
  <p>Is it not possible to set the <code>epsilon</code> parameter when using the ensemble method? e.g.</p>
  
  <pre><code>from sklearn import ensemble
  params = {'n_estimators': 300, 'max_depth': 3, 'min_samples_split': 2,
            'learning_rate': 0.01, 'loss': 'huber', 'epsilon':1.0, 'alpha':.5, 'criterion':'mse', }
  clf = ensemble.GradientBoostingRegressor(**params)
  </code></pre>
  
  <p>gives:</p>
  
  <pre><code>Traceback (most recent call last):
  
    File "&lt;ipython-input-6-875d10cdac68&gt;", line 3, in &lt;module&gt;
      clf = ensemble.GradientBoostingRegressor(**params)
  
  TypeError: __init__() got an unexpected keyword argument 'epsilon'
  </code></pre>
  
  <p>Even this section <a href="https://scikit-learn.org/stable/modules/ensemble.html#loss-functions" rel="nofollow noreferrer">1.11.4.5.1. Loss Functions</a> says to use <code>alpha</code> to control the robustness to outliers, but this is different from  <code>epsilon</code>...</p>