title: Why am I getting very little variance in predict_proba values in XGBoost?
body: <p>I'm having trouble understanding why all the values when calling the <code>predict_proba</code> function in the xgboost library in python are in a quite close range of values, even though the model AUC in the test set is good enough for the problem at hand (0.78).</p>
  <p><a href="https://i.stack.imgur.com/92jGx.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/92jGx.png" alt="" /></a></p>
  <p>As you can see, the variance is low and the results are quite near around the 50% mark.</p>
  <p>The test size is approximately a 15% of the available data (5000 observations).</p>
  <p>I'm using the following parameters:</p>
  <pre><code>{'colsample_bytree': 0.5, 'gamma': 2, 'learning_rate': 0.01, 'max_depth': 8, 'min_child_weight': 10,
                  'n_estimators': 10, 'scale_pos_weight': 7}
  </code></pre>
  <p>Am I missing something here?</p>