What is Ensemble Learning? Types of Ensemble Learning

We train machine learning models to make the best predictions possible for a given problem. On the other hand, a single model may not make the best predictions and may be prone to errors such as variance and bias.

 

We can combine multiple models into a single model to reduce these errors and improve predictions. This is called ensemble learning. We will investigate ensemble learning techniques for improving the machine learning process.

 

What is Ensemble Learning?

 

Ensemble learning is the process of strategically creating and combining multiple models, such as classifiers or experts, to solve a specific computational intelligence problem.

 

 Ensemble learning is primarily used to improve the performance of a model (classification, prediction, function approximation, etc.) or to reduce the likelihood of an unintentional selection of a poor one.

 

Other uses for ensemble learning include assigning a confidence level to the model's decision, selecting optimal (or near-optimal) features, data fusion, incremental learning, nonstationary learning, and error correction.

 

This article focuses on classification-related ensemble learning applications; however, all of the principles described below can be easily generalized to function approximation or prediction-type problems as well.

 

How does Ensemble Learning Work?

 

Assume you want to create a machine learning model that predicts inventory stock orders for your company based on historical data from previous years.

 

Assume you want to create a machine learning model that predicts inventory stock orders for your company based on historical data from previous years. You train four machine learning models using different algorithms:

 

linear regression, support vector machine, regression decision tree, and basic artificial neural network. However, even after extensive tweaking and configuration, none of them achieve the desired 95 percent prediction accuracy. These machine learning models are referred to as "weak learners" because they fail to converge to the desired level.

 

However, being weak does not imply being useless. You can put them together to form an ensemble. For each new prediction, you run your input data through all four models and then average the results.

 

When you examine the new result, you will notice that the aggregate results provide 96 percent accuracy, which is more than satisfactory. Because your machine learning models work differently, ensemble learning is efficient. Each model may perform well on some data but not on others. When all of them are combined, their weaknesses cancel each other out.

 

Ensemble methods can be applied to both prediction problems, such as the inventory prediction example we just saw, and classification problems, such as determining whether a picture contains a specific object.

 

Also Read | 5 Machine Learning Techniques to Solve Overfitting

 

Why use Ensemble Learning?

 

For several reasons, an ensemble is preferable to a single model:

 

1. Performance: As described in the preceding section, the outcome of ensemble learning is a strong learner. The strong learner is the result of weak learners. As a result, models' predictive capabilities improve. Better performance is achieved when compared to a single model.

 

2. Error reduction: Machine learning model prediction errors can be described by bias and variance. Bias is defined as the difference between a prediction and the actual outcome. Variance is defined as a model's sensitivity to small changes in the training set. A model with low bias and variance is preferable, but it is difficult to achieve in practice.

 

Ensemble methods can help to reduce prediction variance. For example, the spread of performance can be contained by averaging the performance of various models. The bagging technique helps to reduce variance.

 

Types of Ensemble Learning Methods

 

Below are the advanced types of ensemble learning methods :

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Types of Ensemble Learning

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1. Bagging

 

Bagging, short for bootstrap aggregation, is primarily used in classification and regression. It improves model accuracy by using decision trees, which greatly reduces variance. The elimination of overfitting, which is a challenge for many predictive models, increases accuracy by reducing variance.

 

There are two types of bagging: bootstrapping and aggregation.

 

Bootstrapping is a sampling technique that uses the replacement procedure to obtain samples from the entire population (set). 

 

In bagging, aggregation is used to incorporate all possible outcomes of the prediction and randomize the outcome. As a result, the aggregation is based on probability bootstrapping procedures or on all predictive model outcomes.

 

2. Boosting

 

Boosting is an ensemble technique that learns from previous predictor errors in order to make better predictions in the future. The technique combines several weak base learners to form one strong learner, significantly improving model predictability.

 

Boosting works by arranging weak learners in a sequence so that weak learners can learn from the next learner in the sequence to create better predictive models.

 

Gradient boosting, adaptive boosting (AdaBoost), and XGBoost are all types of boosting (Extreme Gradient Boosting).

 

AdaBoost employs weak learners in the form of decision trees, which typically include only one split, also known as decision stumps. New predictors are fitted to compensate for errors in previous predictors. The gradient of descent aids the gradient booster in identifying and correcting errors in learners' predictions.

 

XGBoost improves speed and performance by utilizing decision trees with boosted gradients. It heavily relies on the target model's computational speed and performance. Model training should be done in a specific order, making gradient boosted machine implementation time-consuming.

 

3. Stacking

 

Another ensemble method is stacking, which is also known as stacked generalization. This technique works by allowing a training algorithm to combine the predictions of several other similar learning algorithms. 

 

Stacking has been used successfully in regression, density estimation, distance learning, and classification. It can also be used to calculate the error rate during bagging.

 

Basic Types of Ensemble Learning

 

Below are the 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. 

 

Also Read | What is AdaBoost Algorithm in Ensemble Learning?

 

 

Challenges of Ensemble Learning

 

While ensemble learning is a powerful tool, and as fascinating as it can look, it still does have some drawbacks. So below are some of the challenges faced by ensemble learning.

 

When you use an ensemble, you must devote more time and resources to training your machine learning models. A random forest with 500 trees, for example, produces much better results than a single decision tree, but it also takes much longer to train. Running ensemble models can also be difficult if the algorithms you use require a large amount of memory.

 

Explainability is another issue with ensemble learning. While adding new models to an ensemble can improve its overall accuracy, it makes it more difficult to investigate the decisions of the AI algorithm. 

 

A single machine learning model, such as a decision tree, is simple to trace. However, when hundreds of models contribute to an output, it becomes much more difficult to understand the logic behind each decision.

 

Ensemble, like most things in machine learning, is one of many tools available to you for solving complex problems. It can help you get out of difficult situations, but it is not a panacea. Make good use of it.

 

Ensemble Learning Applications

 

Ensemble learning is a fairly common deep learning strategy that has been used to solve a wide range of problems. It has aided in the completion of complex pattern recognition tasks that require computers to learn high-level semantic information from digital images or videos, such as object detection, which requires the formation of bounding boxes around the objects of interest, and image classification.

 

Here are some examples of Ensemble Learning in action.

 

1. Remote Sensing

 

Monitoring the physical characteristics of a target area without coming into physical contact, known as remote sensing, is a difficult task because the data acquired by different sensors has varying resolutions, resulting in data distribution incoherence.

 

Ensemble Learning has also been used to perform tasks such as landslide detection and scene classification.

 

2. Emotion recognition in Speech

 

Ensemble learning is also used in speech emotion recognition, particularly in multilingual environments. Instead of selecting one classifier and compromising the accuracy of a specific language corpus, the technique allows for the combining of all classifiers' effects.

 

3. Detection of diseases

 

Ensemble learning has aided in disease classification and localization for simplified and rapid prognosis, such as in cardiovascular disease detection from X-Ray and CT scans.

 

4. Detection of Fraud

 

Because very fine precision is required to automate the process, detecting digital fraud is an important and difficult task. Ensemble Learning has been shown to be effective in detecting credit card and impression fraud.

 

Ensemble learning is a common machine learning technique that involves combining the predictions of multiple experts (classifiers). The need for ensemble learning arises in a variety of problematic situations that can be both data-centric and algorithm-centric, such as data scarcity/excess, problem complexity, computational resource constraints, and so on.

 

Several methods developed over the years have proven useful in addressing a variety of such issues. Nonetheless, researchers are developing newer ensemble approaches that address the shortcomings of traditional ensembles.