In this chapter, we learnt about a very popular approach called ensembling in machine learning. We learnt how a group of decision trees can be parallelly built, trained, and run on a dataset in the case of random forests. Finally, their results can be combined by techniques like voting for classification to figure out the best voted classification or averaging the results in case of regression. We also learnt how a group of weak decision tree learners or models can be sequentially trained one after the other with every step boosting the results of the previous model in the workflow by minimizing an error function using techniques such as gradient descent. We also saw how powerful these approaches are and saw their advantages over other simple approaches. We also ran the two ensembling approaches on a real-world dataset provided by Lending Club and analyzed the accuracy of our results.
In the next chapter, we will cover the concept of clustering using the k-means algorithm. We will...
