Ensemble learning is used in ML to acquire crowd wisdom. The outcome of an ensemble, which is a collection of machine learning models, can be more accurate than any one member of the group for many tasks.
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 techniques to train four machine learning models: linear regression, support vector machine, regression decision tree, and basic artificial neural network. However, after extensive tuning and setting, none of them meets your target forecast accuracy. 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 may put them together to make an outfit. You run your input data through all four models for each new prediction, 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 doing poorly on others. When all of them are combined, their flaws are balanced out.
Ensemble techniques may be used to solve both prediction and classification issues, such as assessing whether an image includes a certain object, as in the inventory prediction example we just saw.
You must ensure that your models are independent of one another while creating a machine learning ensemble (or as independent of each other as possible). As seen in the example above, one approach to achieve this is to build your ensemble from multiple algorithms.
Another ensemble strategy is to train separate data sets with different instances of the same machine learning algorithms.
For sampling data from your training set, there are two main approaches. “Bootstrap aggregation,” often known as “bagging,” involves taking random samples from the training set and replacing them with new ones. The “pasting” approach, on the other hand, draws samples “without replacement.”
You’ll need to pick 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 used aggregate approach. In regression problems, ensembles often utilize the average of the models’ predictions.
Difficulties with ensemble learning
While ensemble learning is a wonderful technique, it does come with certain disadvantages.
When you use ensembles, you’ll have to devote more time and resources to train your machine learning models. A random forest with 1000 trees, for example, produces significantly better results than a single decision tree, but it takes much longer to train. If the techniques you’re using demand a lot of memory, running ensemble models might be difficult.
Explainability is another issue with ensemble learning. While adding new models to an ensemble might enhance overall accuracy, it also makes it more difficult to analyze the AI algorithm’s conclusions. A single machine learning model, such as a decision tree, is simple to trace, but when there are hundreds of models contributing to output, understanding the rationale behind each choice becomes considerably more challenging.
The ensemble is one among the numerous tools you have for addressing difficult issues, just like virtually everything else in machine learning. It can help you get out of a tight spot, but it’s not a panacea. Make good use of it.