Use ensemble methods to combine output of more than one models?

This is the most common approach found majorly in winning solutions of Data science competitions. This technique simply combines the result of multiple weak models and produce better results. This can be achieved through many ways:

• Bagging (Bootstrap Aggregating)
• Boosting

To know more about these methods, you can refer article “Introduction to ensemble learning“ .

It is always a better idea to apply ensemble methods to improve the accuracy of your model. There are two good reasons for this: * They are generally more complex than traditional methods * The traditional methods give you a good base level from which you can improve and draw from to create your ensembles.

Taking the average of predictions (given by different models) is an example of ensemble model?