What Is Ensemble Learning? Understanding Machine Learning Techniques

If you are a beginner who wants to understand in detail what is ensemble, or if you want to refresh your knowledge about variance and bias, the comprehensive article below will give you an in-depth idea of ensemble learning, ensemble methods in machine learning, ensemble algorithm, as well as critical ensemble techniques, such as boosting and bagging. But before digging deep into the what, why, and how of ensemble, let's first take a look at some real-world examples that will simplify the concepts that are at the core of ensemble learning.

Example 1: If you are planning to buy an air-conditioner, would you enter a showroom and buy the air-conditioner that the salesperson shows you? The answer is probably no. In this day and age, you are likely to ask your friends, family, and colleagues for an opinion, do research on various portals about different models, and visit a few review sites before making a purchase decision. In a nutshell, you would not come to a conclusion directly. Instead, you would try to make a more informed decision after considering diverse opinions and reviews. In the case of ensemble learning, the same principle applies. Now let's see what ensemble means.

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What Is Ensemble?

The ensemble methods in machine learning combine the insights obtained from multiple learning models to facilitate accurate and improved decisions. These methods follow the same principle as the example of buying an air-conditioner cited above.

In learning models, noise, variance, and bias are the major sources of error. The ensemble methods in machine learning help minimize these error-causing factors, thereby ensuring the accuracy and stability of machine learning (ML) algorithms.

Example 2: Assume that you are developing an app for the travel industry. It is obvious that before making the app public, you will want to get crucial feedback on bugs and potential loopholes that are affecting the user experience. What are your available options for obtaining critical feedback? 1) Soliciting opinions from your parents, spouse, or close friends. 2) Asking your co-workers who travel regularly and then evaluating their response. 3) Rolling out your travel and tourism app in beta to gather feedback from non-biased audiences and the travel community.

Think for a moment about what you are doing. You are taking into account different views and ideas from a wide range of people to fix issues that are limiting the user experience. The ensemble neural network and ensemble algorithm do precisely the same thing.

Example 3: Imagine a group of blindfolded people playing the touch-and-tell game, where they are asked to touch and explore a mini donut factory that no one of them has ever seen before. Since they are blindfolded, their version of what a mini donut factory looks like will vary, depending on the parts of the appliance they touch. Now, suppose they are personally asked to describe what they touched. In that case, their individual experiences will give a precise description of specific parts of the mini donut factory. Still, collectively, their combined experiences will provide a highly detailed account of the entire equipment.

Similarly, ensemble methods in machine learning employ a set of models and take advantage of the blended output, which, compared to a solitary model, will most certainly be a superior option when it comes to prediction accuracy.

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Ensemble Techniques

Here is a list of ensemble learning techniques, starting with basic ensemble methods and then moving on to more advanced approaches.

Simple Ensemble Methods

Mode: In statistical terminology, "mode" is the number or value that most often appears in a dataset of numbers or values. In this ensemble technique, machine learning professionals use a number of models for making predictions about each data point. The predictions made by different models are taken as separate votes. Subsequently, the prediction made by most models is treated as the ultimate prediction.

The Mean/Average: In the mean/average ensemble technique, data analysts take the average predictions made by all models into account when making the ultimate prediction.

Let's take, for instance, one hundred people rated the beta release of your travel and tourism app on a scale of 1 to 5, where 15 people gave a rating of 1, 28 people gave a rating of 2, 37 people gave a rating of 3, 12 people gave a rating of 4, and 8 people gave a rating of 5.

The average in this case is - (1 * 15) + (2 * 28) + (3 * 37) + (4 * 12) + (5 * 8) / 100 = 2.7

The Weighted Average: In the weighted average ensemble method, data scientists assign different weights to all the models in order to make a prediction, where the assigned weight defines the relevance of each model. As an example, let's assume that out of 100 people who gave feedback for your travel app, 70 are professional app developers, while the other 30 have no experience in app development. In this scenario, the weighted average ensemble technique will give more weight to the feedback of app developers compared to others.

Advanced Ensemble Methods

Bagging (Bootstrap Aggregating): The primary goal of "bagging" or "bootstrap aggregating" ensemble method is to minimize variance errors in decision trees. The objective here is to randomly create samples of training datasets with replacement (subsets of the training data). The subsets are then used for training decision trees or models. Consequently, there is a combination of multiple models, which reduces variance, as the average prediction generated from different models is much more reliable and robust than a single model or a decision tree.

Boosting: An iterative ensemble technique, "boosting," adjusts an observation's weight based on its last classification. In case observation is incorrectly classified, "boosting" increases the observation's weight, and vice versa. Boosting algorithms reduce bias errors and produce superior predictive models.

In the boosting ensemble method, data scientists train the first boosting algorithm on an entire dataset and then build subsequent algorithms by fitting residuals from the first boosting algorithm, thereby giving more weight to observations that the previous model predicted inaccurately.

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