title: SelectKBest for regression gives "unknown label type"-error
body: <p>I am trying to get a slightly modified version of the SelectKBest example to work but keep getting a ValueError("Unknown label type: %s" % repr(ys))</p>
  
  <p>Here's my code:</p>
  
  <pre><code># Importing dependencies
  import numpy as np
  from sklearn.feature_selection import SelectKBest
  from sklearn.feature_selection import chi2
  from sklearn.datasets import load_iris
  
  #The Example from:
  #http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
  iris = load_iris()
  X, Y = iris.data, iris.target
  print(X.shape, type(X), type(X[0,0]))
  print(Y.shape, type(Y), type(Y[0]))
  X_new = SelectKBest(chi2, k=2).fit_transform(X, Y)
  
  #My toyproblem:
  X = np.random.uniform(0,1, size=(5000, 10))
  Y = np.random.uniform(0,1, size=(5000,))
  
  #Type cast which might solve my problem by thi suggestion:
  # https://stackoverflow.com/questions/45346550/valueerror-unknown-label-type-unknown
  X=X.astype('float')
  Y=Y.astype('float')
  
  print(X.shape, type(X), type(X[0,0]))
  print(Y.shape, type(Y), type(Y[0]))
  
  X_new = SelectKBest(chi2, k=2).fit_transform(X, Y)
  </code></pre>
  
  <p>The input data is shaped in exactly the same way, also input data types are almost the same:</p>
  
  <pre><code>(150, 4) &lt;class 'numpy.ndarray'&gt; &lt;class 'numpy.float64'&gt;
  (150,) &lt;class 'numpy.ndarray'&gt; &lt;class 'numpy.int32'&gt;
  (5000, 10) &lt;class 'numpy.ndarray'&gt; &lt;class 'numpy.float64'&gt;
  (5000,) &lt;class 'numpy.ndarray'&gt; &lt;class 'numpy.float64'&gt;
  </code></pre>
  
  <p>However my code crashes throwing the mentioned ValueError. Reading the SelectKBest Doc, the switch from classification to regression shouldn't really be a problem. Can someone help me find out what's going wrong?</p>
  
  <pre><code>Traceback (most recent call last):
    File "featureSelection_toyproblem.py", line 26, in &lt;module&gt;
      X_new = SelectKBest(chi2, k=2).fit_transform(X, Y)
    File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib
  \site-packages\sklearn\base.py", line 520, in fit_transform
      return self.fit(X, y, **fit_params).transform(X)
    File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib
  \site-packages\sklearn\feature_selection\univariate_selection.py", line 349, in
  fit
      score_func_ret = self.score_func(X, y)
    File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib
  \site-packages\sklearn\feature_selection\univariate_selection.py", line 217, in
  chi2
      Y = LabelBinarizer().fit_transform(y)
    File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib
  \site-packages\sklearn\preprocessing\label.py", line 307, in fit_transform
      return self.fit(y).transform(y)
    File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib
  \site-packages\sklearn\preprocessing\label.py", line 284, in fit
      self.classes_ = unique_labels(y)
    File "C:\Users\mobrecht\AppData\Local\Continuum\anaconda3\envs\env_zipline\lib
  \site-packages\sklearn\utils\multiclass.py", line 97, in unique_labels
      raise ValueError("Unknown label type: %s" % repr(ys))
  ValueError: Unknown label type: (array([ 0.42595241,  0.79859991,  0.22947246, .
  ..,  0.86011766,
          0.52335991,  0.27046173]),)
  </code></pre>