#ScikitLearn #EnsembleModels #MachineLearning #DataAnalytics #DataScience
In this tutorial, we implement ensemble models using scikit learn and test the improvement in accuracy and other advantages of using ensemble methods.
We implement scikit learn bagging classifier, scikit learn adaboost classifier (boosting) and scikit learn voting classifier (bagging).
Ensemble Learning is using multiple learning algorithms at a time, to obtain predictions with an aim to have better predictions than the individual models.
Ensemble learning is a very popular method to improve the accuracy of a machine learning model.
It avoid overfitting and gives us a much better model.
bootstrap aggregating (Bagging) and boosting are popular ensemble methods.
For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon
Facebook : https://www.facebook.com/thesemicolon.code
Support us on Patreon : https://www.patreon.com/thesemicolon
