title: Can xgboost (or any other algorithm) give bad results with some bad features?
body: <p>till now I was under the impression that machine learning algorithms (gbm, random forest, xgboost etc) can handle bad features (variable) present in the data. </p>
  
  <p>In one of my problems, there are around 150 features and with xgboost I am getting a logloss of around 1 if I use all features. But if I remove around 10 bad features (found using some technique) I am observing a logloss of .45. That is huge improvement.</p>
  
  <p>My question is, can bad features really make such big differences?</p>