title: Why I'm getting small score of correlation and mutual information?
body: <p>I'm generating random samples for binary classification problem:</p>
  
  <pre><code>X, y = make_classification(n_features=40, n_redundant=4, n_informative=36,n_clusters_per_class=2, n_samples=50000)
  </code></pre>
  
  <p>I want to check the correlation between the features and the target (for <code>feature selection</code> step).</p>
  
  <p>I'm using 2 different methods:</p>
  
  <pre><code>1. correlation (pearson)
  2. mutual information
  </code></pre>
  
  <p>I'm getting small score, for both methods between the features and the target.</p>
  
  <p>Mutual information:</p>
  
  <pre><code>    from sklearn.feature_selection import mutual_info_classif
      res1 = mutual_info_classif(X, y)
  </code></pre>
  
  <p>Correlation:</p>
  
  <pre><code>df = pd.DataFrame(data=X)
  df['Taregt'] = y
  res2 = df.apply(lambda x: x.corr(df['Taregt']))
  </code></pre>
  
  <p>For both methods I'm getting results which are less than 0.4</p>
  
  <p>Why am I getting small score ? I expect to get higher score ? 
  What am I missing ?</p>