Ensemble learning is a generic approach to machine learning that aims to improve predictive performance by mixing predictions from several models.
Despite the fact that you can create an almost infinite number of ensembles for any predictive modeling problem, there are three strategies that dominate the field of ensemble machine learning. So much so that instead of algorithms as such, each is a subject of study that has given rise to other more specialized approaches.
- Using ensemble approaches, a single best-fit prediction model can be generated by combining multiple basic models.
Bagging, stacking, and boosting are the three basic types of ensemble learning techniques, and understanding each approach in depth and incorporating it into your predictive modeling project is critical.
Importance of ensemble learning
Assume you want to create a machine learning model for your organization that forecasts inventory stock orders based on historical data from prior years. You employ four distinct algorithms to train four machine learning models: linear regression, support vector machine, regression decision tree, and basic artificial neural network. However, despite extensive tuning and configuration, none of them achieves your desired forecast accuracy of 95%. Because they fail to converge to the target level, these machine learning models are referred to as “weak learners.”
Weakness, on the other hand, does not imply ineffectiveness. You can put them together to make an ensemble. You run your input data through all four models for each new ensemble prediction in machine learning, then take the average of the results. When you look at the new result, you’ll notice that the overall accuracy is 96 percent, which is more than adequate.
Because your machine learning models perform differently, ensemble learning is efficient. Each model may perform well on certain data while performing poorly on others. When all of them are combined, their flaws are balanced out.
- Ensemble methods can be used to solve both prediction and classification problems.
Ensemble methods in Machine Learning
To use a machine learning ensemble, your models must be independent of one another. Creating an ensemble from different algorithms is one method to go about it.
Another ensemble strategy is to train various instances of the same ensemble algorithm in machine learning on distinct data sets.
Data sampling from your training set can be accomplished in two ways. “Bagging,” also known as “bootstrap aggregation,” uses random samples from the training set. Pasting, on the other hand, takes samples “without replacement.”
Here’s an ensemble learning example to help clarify the differences between the various sampling techniques. So, let’s say that you want to train each machine learning model in your ensemble with 5,000 data, and you have a training set of 4,000 samples.
If you’re bagging, you’ll need to do the following for each of your machine learning models:
- Take a random sample from the training data.
- Make a duplicate of the sample and save it in the model’s data set.
- Reintroduce the sample to the original test set.
- Rep the procedure 3,999 times
The technique is the same when employing pasting, with the exception that samples are not returned to the training set after being drawn. As a result, while bagging, the same sample may appear multiple times in a model, but only once when pasting.
You’ll need to choose an aggregate approach after you’ve trained all of your machine learning models. When dealing with a classification problem, the statistical model, or the class that is predicted more than others, is the most common aggregate approach. In regression problems, ensembles typically utilize the average of the models’ predictions.
Boosting is another famous ensemble approach. Boosting approaches, in contrast to traditional ensemble methods, which train machine learning models in parallel, train them sequentially, with each new model building on the prior one and solving its inefficiencies.
One of the more common boosting strategies, AdaBoost (short for “adaptive boosting”), enhances the accuracy of ensemble models in machine learning by adapting new models to the faults of prior ones. Following the training of your first machine learning model, you identify the training instances that the model misclassified or forecasted incorrectly. You emphasize these examples more in the following model’s training. As a result, a machine learning model that outperforms the previous one is created. The technique can be repeated as many times as you like to add more models to the ensemble. The final ensemble consists of several machine learning models with varying degrees of accuracy that, when combined, can improve accuracy. In boosted ensembles, each model’s output is given a weight proportional to its accuracy.
Ensemble, like most things in machine learning, is one of several tools available to you for addressing complex problems. It can help you get out of difficult situations, but it is not a miracle. Make good use of it.