title: Why Information gain feature selection gives zero scores
body: <p>I have a dataset in which I used the Information gain feature selection method in WEKA to get the important features. Below is the output I got.</p>
  
  <pre><code>Ranked attributes:
  0.97095    1 Opponent
  0.41997   11 Field_Goals_Made
  0.38534   24 Opp_Free_Throws_Made
  0.00485    4 Home
  0          8 Field_Goals_Att
  0         12 Opp_Total_Rebounds
  0         10 Def_Rebounds
  0          9 Total_Rebounds
  0          6 Opp_Field_Goals_Made
  0          7 Off_Rebounds
  0         14 Opp_3Pt_Field_Goals_Made
  0          2 Fouls
  0          3 Opp_Blocks
  0          5 Opp_Fouls
  0         13 Opp_3Pt_Field_Goals_Att
  0         29 3Pt_Field_Goal_Pct
  0         28 3Pt_Field_Goals_Made
  0         22 3Pt_Field_Goals_Att
  0         25 Free_Throws_Made
  </code></pre>
  
  <p>Which tells me that all features with score 0 can be ignored, is it correct?</p>
  
  <p>Now when I tried the Wrapper subset evaluation in WEKA, I got selected attribute which were ignored in info gain method (i.e whose score was 0). Below is the output</p>
  
  <pre><code>Selected attributes: 3,8,9,11,24,25 : 6
                   Opp_Blocks
                   Field_Goals_Att
                   Total_Rebounds
                   Field_Goals_Made
                   Opp_Free_Throws_Made
                   Free_Throws_Made
  </code></pre>
  
  <p>I want to understand, what is the reason that the attributes ignored by info gain are considered strongly by wrapper subset evaluation method?</p>