Ensemble Methods — Combining Models for Achieving Better Accuracy
In this article, we will talk about Ensemble Methods, what are its types, and how they can be used for achieving better accuracy.
Whenever we talk about Machine Learning, the first thought that comes to our mind is that we have to train a model for predicting outcomes, etc. but there are a lot of cases when Machine Learning Models do not provide better accuracy in terms of outcomes/predictions.
There are many ways to enhance your model to provide better accuracy and one of them is to use Ensemble Methods.
What are Ensemble Methods?
Ensemble Methods are Machine Learning Techniques that can provide you a better accuracy by combining several base models instead of using a single model. Using Ensemble Methods, your machine learning model can produce significantly more accurate results.
Usually, Ensemble Methods are ideal when you have to deal with Regression and Classification because they can increase the accuracy of your model and reduce its bias.
Types of Ensembles Methods
The most popular ensemble methods are Bagging and Boosting which fall in the paradigms named Parallel and Sequential respectively.
If you have heard about Bagging and Boosting at some stage but don’t know what it does, Don’t worry. Let’s deep dive into the popular ensemble methods and understand them.
Bagging
Bagging is also known as Bootstrap Aggregating, falls under the category of Parallel ensemble method, is a way to reduce the variance of the Machine Learning Model by generating the additional data in the training stage.
As mentioned above Bagging is also known as Bootstrap Aggregating so Bootstrapping and Aggregation itself are two separate techniques.
Bootstrapping is a sampling technique that creates many simulated samples from a single dataset whereas Aggregation is to incorporate all possible outcomes of the prediction and randomize the outcome. However, if the Aggregation technique is missed then the predictions will not be accurate because all the outcomes of your model will not be considered.
Significantly reducing the variance which will result in the elimination of Overfitting, hence we can have much better accuracy of the predictive model using Bagging.
Boosting
Boosting is a Sequential ensemble method technique that learns from the previous predictor mistakes to make better predictions in the future.
The main idea of Boosting technique is to combine several weak base learners to create one strong learner, hence making a great predictive machine learning model.
Boosting can be achieved with a very well-known algorithm known as Adaptive Boosting (AdaBoost). The technique of AdaBoost is that the weights are re-assigned to each instance, with higher weights assigned to incorrectly classified instances.
Boosting incrementally builds an ensemble by training each model with the same dataset but where the weights of instances are adjusted according to the error of the last prediction thus reducing bias and variance and providing a model with enhanced accuracy.
Conclusion
The goal that needs to be achieved in the domain of Machine Learning is to create models that can best predict the outcomes. By significantly reducing the bias and variance, Ensemble Methods become really important and can be of great help for achieving an improved accuracy.
