Ensemble Learning Method
Ensemble Learning Method

What is Ensemble Learning Method?

The ensemble learning method is a machine learning technique that combines several base models to create a better predictive model.

How does Ensemble Learning work?

Say you want to develop a machine learning model that predicts your company’s inventory stock orders based on historical data you have accumulated over the years. You use the train for machine learning model using different algorithms: linear, regression, support vector machine, regression decision tree, and a basic artificial neural network. But even after much adaptation and configuration, none of them can achieve the95% accuracy of your prediction. These machine learning models are called ‘’weak learners’’ because they fail to reach the desired level.

Ensemble learning
Ensemble learning

Single machine learning models do not provide the required accuracy. But weak does not mean useless. You can combine them. For each new prediction, you run your input data through all four models, and then calculate the average of the results. When checking the new results, you will find that the overall results provide 96% accuracy, which is more than acceptable.

The reason why ensemble learning is so effective is that your machine learning models work differently. Each model can perform well on some data and less accurate on others. When you combine them all, they eliminate each other’s weaknesses?

Types of Ensemble Learning Methods:

There are many Ensemble techniques available but we will discuss them below:

Bagging:

The bagging (or bootstrap aggregate) technique uses these subsets (bags) to get a fair idea of the distribution. The size of the subsets created for bagging may be smaller than the original set.

Boosting

Like bagging, boosting also ensemble the data to form a pair of rankings, which are then obtained by a majority vote. However, in boosting, the most informative training data is provided for each continuous ranking according to the redesign strategy.

Stacking

Stacking (sometimes called stacked generalization) involves training learning algorithms to gather predictions from many other learning algorithms. First, all other algorithms are trained using the available data, then a combinator algorithm is trained to make final predictions using all the other algorithm predictions as additional inputs. If an arbitrary combiner algorithm is used, stacking can theoretically represent any of the pairing techniques described in this article, although, in practice, a logistic regression model is often used as the user Is.

Blending

Blending follows the same approach as stacking but uses set-to-hold (confirmation) from the train seat to make predictions. In other words, unlike stacking, predictions are made only on the whole outset. Hold outsets and predictions are used to create a model that runs on a test set.

Example of Ensemble Learning Method:

Assume, you want to invest in an XYZ company. Although you are not sure about its performance. So, do you consult about whether the stock price will increase by more than 6% per annum or not? You decide to consult a number of experts with diverse domain experience:

An employee of Company XYZ:

This person knows the internal activities of the company and has insider information about the activities of the firm. But it lacks a broad perspective on how competitors are innovating, how technology is evolving, and how this evolution will impact XYZ’s products. In the past, he has recovered 70 times.

Financial Advisor of Company XYZ:

This person has a broad perspective on how companies’ strategies will be fair in this competitive environment. However, it does not consider the company’s internal policies to be fair. In the past, he has recovered by 75%.

Stock Market Trader:

This person has seen the stock price of the company in the last 3 years. He knows the weather trends and how the market as a whole is performing. He also takes a hard look at how stocks can change over time. In the past, he has recovered 70 times.

An employee of a competitor:

This person knows the internal functioning of competing firms and is aware of some of the changes that remain to be made. It lacks the attention and focus of the company on external factors related to the development of competitors with the company of subjects. In the past, he has recovered 60 times.

Market Research team in the same segment:

This team analyzes XYZ’s company’s customer preference and how it has changed over time compared to others. Since he deals with the customer, he is not unaware that XYZ will bring XYZ because of its alignment with its own goals. In the past, they have been correct 75% of the time.

Social Media Expert:

This person can help us understand how the company XYZ has positioned its products in the market. And how are consumer sentiments about the company changing over time? He is unaware of any details beyond digital marketing. In the past, he has been exactly 65% of the time.

You may also like to read: What is a Virtual Agent? How Virtual Agent Work?

 

 

 

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