Ensemble Learning

One of the most powerful machine learning techniques is ensemble learning. Ensemble Learning  is the use of multiple machine learning models to improve the reliability and accuracy of predictions. Yet how does the use of multiple machine learning models lead to more accurate predictions? What kind of techniques are used to create ensemble learning models? We’ll explore the answer to these questions, taking a look at the rationale behind using ensemble models and the primary ways of creating ensemble models.

What Is Ensemble Learning?

Put simply, ensemble learning is the process of training multiple machine learning models and combining their outputs together. The different models are used as a base to create one optimal predictive model. Combining a diverse set of individual machine learning models can improve the stability of the overall model, leading to more accurate predictions. Ensemble learning models are frequently more reliable than individual models, and as a result, they often place first in many machine learning competitions.

Simple Ensemble Training Methods

Simple ensemble training methods typically just involve the application of statistical  summary tecnniques, such as determining the mode, mean, or weighted average of a set of predictions.

Mode refers to the most frequently occurring element within a set of numbers. In order to get the mode, the individual learning models return their predictions and these predictions are considered votes toward the final prediction. Determining the mean of the predictions is done simply by calculating the arithmetic mean of the predictions, rounded to the nearest whole integer. Finally, a weighted average can be calculated by assigning different weights to the models used to create predictions, with the weights representing the perceived importance of that model. The numerical representation of the class prediction is multiplied alongside a weight from 0 to 1.0, the individual weighted predictions are then summed together and the result is rounded to the nearest integer.

he 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

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.

Basic Types of Ensemble Learning

1. Averaging

This is the simplest ensemble technique to define because it simply averages all of the models' predictions. The final prediction is made using the average of the predictions.

This method is applicable to classification and regression problems. Consider the previous section's scenario, where we want to forecast car prices. Our models forecast $ 6000, 7000, 5000, and 6000.

Since we’re averaging, the final prediction is:

 

6000+7000+5000+6000 ÷ 4=6000

 

2. Max Voting Classifier

This method is similar to the average, but it is best suited for classification problems. Its explanation is straightforward.

Several models make predictions known as "votes"; each prediction counts as one vote. As is often the case with voting, decisions are frequently made in favor of the majority of votes. The same is true here. The majority of classifiers' predictions become the final prediction.

 

For example, we try to forecast car prices. The models involved yield prices (in dollars) such as 6000, 5000, 8000, 5000, 6000, 5000, 7000, 5000, and 7000. We notice that the majority of the models forecast $5000. 5000 is the final prediction using maximum voting.

 

3. Weighted Averaging

This method is a variant of the averaging method. The difference is that, whereas averaging gives equal weight to all models, a weighted average gives more weight to a model with greater predictive power. This significance is represented by weights. These weights can be represented as decimals with a total of one. 

I hope you understood a basic about Ensemble Learning.