title: Random Forest Classifier: Unrealistic predictions after changing inference data column order to trainig data column order
body: <p>I have a RandomForest classification model trained with (30000 x 164) data (train-70%, test-30%).</p>
  <pre><code>  **RandomForestClassifier(n_estimators= 200, max_features= 'sqrt', random_state=40)**
         test results= Sensitivity - 75,   Specificty - 99
  </code></pre>
  <p>Because I have imbalanced classes(1s-10%, 0s-90%), I had to get the probabilities and extract the classes based on a threshold value probability(0.25 in my case) instead of the default 0.5.</p>
  <pre><code>    test results with 0.25 probability threshold = Sensitivity - 91,   Specificty - 96
  </code></pre>
  <p>Now, I have a new inference dataset (168x164) I want to predict the classes for, using the saved RF model(mlflow). When I prepare this data and make sure the column order of the inference data match with X_train, the model (threshold=0.25) is predicting 97%-99.5% as a class '1' when I am expecting only 5-10% of the observations as class '1' and the rest as '0'.(the default predictions or for the threshold 0.5 - the model is predicting more than 50% of data as class '1', which is also terrible)
  I checked this for multiple inference datasets, I see the same problem.</p>
  <p>When I do not match the column order of train and inference data, the results are more reasonable compared to the above case.
  I know the column order should match, but am not sure what is causing the issue. Can someone help me with this?</p>
  <p>Please let me if you need more in ormation to understand the problem.
  Thank you in advance.</p>