title: xgboost library 0.9 vs 1.3.3
body: <p>I worked for some time on hyper-parameters tuning on a XGBRegressor based on xgboost library version 0.9 (Python for Anaconda on Windows).
  After installing the latest Anaconda and xgboost(version 1.3.3 this time) on a different PC, I noticed different results on the same code. Here is the simplified code:</p>
  <pre><code>from xgboost import XGBRegressor
  params = {
      'max_depth': 9,
      'n_estimators': 400,
      'learning_rate': 0.1,
      'min_child_weight': 2,
      'reg_alpha': 0.05,
      'reg_lambda': 1,
      'colsample_bytree': .8,
      'colsample_bynode': 1,
      'colsample_bylevel': .9,
      'objective': 'reg:squarederror'
  
  }
  
  model = XGBRegressor(**params)
  model.fit(X, Y)
  </code></pre>
  <p>If I am using model to make predictions, I get different results with xgb 1.3.3. I tried to match the hyper parameters by adding all the new default parameters I found in the 1.3.3 library</p>
  <pre><code>'base_score': 0.5,
  'booster': 'gbtree',
  'gamma': 0,
  'max_delta_step': 0,
  'random_state': 0,
  'scale_pos_weight': 1,
  'subsample': 1,
  'seed': 0
  </code></pre>
  <p>but still the same result. Can you help me adapting the code in order to get the same results on the new environment. I need this to avoid reworking on tuning.</p>
  <p>Thank you!</p>