I surmise that you must be reading a lot about machine learning lately. Allow me to introduce you to the ensemble methods and ensemble learning algorithms in machine learning. Most likely you haven’t even heard about these methods. This is where I step in with the goal of helping you understand ensemble methods in machine learning and what they are used for.
Let’s dive into it!
What are Ensemble Methods in Machine Learning?
Ensemble methods are techniques of machine learning that combine numerous base models to turn into a predictive model. Confused? I am sure you are! Let’s try again in simpler words. Ensemble models are models that create sub-models and then combine all models into one to produce more accurate results.
There are some machine learning competitions where ensemble methods are regularly employed. An example to quote would be the popular Netflix Competition where the winner used an ensemble method to implement a robust filtering algorithm.
Prominent Ensemble Methods
1- Model Averaging
The goal is to make multiple predictions for each set of data in averaging. In this method, an average is being taken of all the predictions which are then used to make a final prediction.
For regression problems, a mean of the predictions from each model is taken. Generally, the choice of using a function to combine the predictions is based on the cost function of the problem.
2- Bagging
It is an ensemble technique that stands for Bootstrap aggregation. It is considered one of the best ways to reduce variance in the estimate. It does this by averaging together multiple estimates.
The bagging technique uses bootstrap sampling to fetch the data in order to train the base learners. To complete this task, bagging uses voting for classification and averaging for regression.
It is also pertinent to mention that a prominent base estimator is a decision tree model that receives higher accuracy as compared to other methods.
It is also one of the ensemble methods that reduce the chance of overfitting complex models. Overfitting is the model that does not generalize from the training data to unseen data.
3- Stacking
Another important ensemble learning algorithm technique is stacking. It combines numerous classification models via a meta-classifier. The base-level models are trained according to a whole training set. After that, the meta-model is trained on the results of the base-level model.
As a matter of fact, the base level mainly comprises different learning algorithms, and consequently, stacking ensembles oftentimes are heterogeneous. It is a frequently used technique for winning the Kaggle data science competition.
4- Boosting
This technique of ensemble methods is utilized to convert weak learners into strong learners. Its principle is that it fits the sequence of weak learners into the weighted data version.
Once this is done, the predictions are then combined together through a weighted majority vote to produce a final prediction. The primary difference between bagging and boosting is that base learners are trained in sequence with the weighted version of the data.
Ensemble Learning Algorithm – the Future!
Ensemble learning algorithm used in machine learning has been highly successful in setting praiseworthy performance on a number of challenging datasets. These are also among the winners of numerous data science competitions.
In addition to the ensemble methods that I have explained in this article, it is common to utilize ensemble methods in deep learning by training accurate and diverse classifiers. Diversity can be achieved by varying hyper-parameter settings, architectures, and training techniques.
If you also want to raise your level of machine learning or deep learning with ensemble methods, reach out to AI engineers at vteams, since ensemble learning algorithms are pretty complex and intricate to implement.
