How to get Predictions with XGBoost and XGBoost using Scikit-Learn Wrapper to match?

by

Please look at this answer here

xgboost.train will ignore parameter n_estimators, while
xgboost.XGBRegressor accepts. In xgboost.train, boosting iterations
(i.e. n_estimators) is controlled by num_boost_round(default: 10)

It suggests to remove n_estimators from params supplied to xgb.train and replace it with num_boost_round.

So change your params like this:

params = {'objective': 'reg:linear', 
      'max_depth': 2, 'learning_rate': .1,    
      'min_child_weight': 3, 'colsample_bytree': .7,
      'subsample': .8, 'gamma': 0, 'alpha': 1}

And train xgb.train like this:

model = xgb.train(dtrain=xgtrain, params=params,num_boost_round=500)

And you will get same results.

Alternatively, keep the xgb.train as it is and change the XGBRegressor like this:

model = XGBRegressor(learning_rate =.1, n_estimators=10,
                     max_depth=2, min_child_weight=3, gamma=0, 
                     subsample=.8, colsample_bytree=.7, reg_alpha=1, 
                     objective="reg:linear")

Then also you will get same results.

More Related Contents:

  1. python kmeans on string
  2. Why does the following code cant get value of n_fold = 1?
  3. How to extract the decision rules from scikit-learn decision-tree?
  4. How to one-hot-encode from a pandas column containing a list?
  5. scikit-learn & statsmodels – which R-squared is correct?
  6. ImportError in importing from sklearn: cannot import name check_build
  7. Principal Component Analysis (PCA) in Python
  8. Random state (Pseudo-random number) in Scikit learn
  9. How can I plot a confusion matrix? [duplicate]
  10. Error importing scikit-learn modules
  11. How to one hot encode variant length features?
  12. pandas dataframe columns scaling with sklearn
  13. Clustering text documents using scikit-learn kmeans in Python
  14. scikit-learn cross validation, negative values with mean squared error
  15. Efficiently create sparse pivot tables in pandas?
  16. ROC for multiclass classification
  17. Passing categorical data to Sklearn Decision Tree
  18. fit-transform on training data and transform on test data [duplicate]
  19. Scikit Learn OneHotEncoder fit and transform Error: ValueError: X has different shape than during fitting
  20. Getting model attributes from pipeline
  21. Efficiently count word frequencies in python
  22. Sklearn Pipeline: Get feature names after OneHotEncode In ColumnTransformer
  23. How to find the importance of the features for a logistic regression model?
  24. Scikit Learn GridSearchCV without cross validation (unsupervised learning)
  25. How to save & load xgboost model? [closed]
  26. Eliminating warnings from scikit-learn [duplicate]
  27. Stratified Sampling in Pandas
  28. sklearn : TFIDF Transformer : How to get tf-idf values of given words in document
  29. In sklearn.decomposition.PCA, why are components_ negative?
  30. Does the SVM in sklearn support incremental (online) learning?

Leave a Comment