How can Businesses switch from Weak Learners to Strong Learners using Machine Learning?

Machine learning's efficient operations have been a boon to businesses. Here's a step-by-step guide to getting a grip on Machine Learning and progressing to the advanced levels.

How can Businesses switch from Weak Learners to Strong Learners using Machine Learning?
Machine Learning weak to strong learners
How can Businesses switch from Weak Learners to Strong Learners using Machine Learning?
How can Businesses switch from Weak Learners to Strong Learners using Machine Learning?

How Businesses can switch from Weak Learners to Strong Learners using Machine Learning?

Machine learning has become one of the foremost widely used tools in this modern world. With the increasing data and the customers getting to crack accurate deals, it has become very essential that machine learning tools help us in recuperating predictions and accurate results from the silos of knowledge. It is being widely used for solving real-life problems which are complex and need good supervision.

Boosting: Key Tool

The ensemble algorithm which works behind the method of converting a weak identity to a stronger identity using Machine Learning to scale back redundancy within the model and make it more precise is understood as Boosting. Ensemble learning is the process of mixing various learners to realize strong and accurate machine learning models.

 

Now you would possibly be wondering what we mean by weak and strong entities. Let’s discuss that also before diving deep into the concepts of boosting.

Weak and Strong learners are the computational tools utilized in ensemble learning techniques. Weak Learners are often mentioned as something better than random guessing of result sets. Now within the case of Strong Learner, the weak learners are combined together to make more accurate results. They are somewhat like a few reconsiderations from the outputs of the weak one only.

 

Also, there are two more terms that seem very confusing in ensemble learning but are easy to grasp, that is, Boosting and Bagging. Bagging is all about combining the same kinds of outputs and is also known as Parallel Ensemble. For instance, the Random Forest algorithm.  Boosting commonly known as Sequential Ensemble and weak entities are not parallel instead sequentially processed. AdaBoost Algorithm is an example of the sequential ensemble.

Boosting algorithm: How does it Work?

As discussed earlier, boosting combines the weak rules to make an accurate strong rule. For locating the weak rules, we follow a basic machine learning algorithm. At each iteration, one weak rule is found which ultimately contributes to the stronger rule. The ML algorithm is as follows:

 

Step 1: With the assistance of the base algorithm, equivalent weight is assigned to each observation.

 

Step 2: When a prediction results in a mistake, greater weight is assigned to it in the next iteration. And now the base algorithm is applied again.

 

Step 3: Step 2 is repeated until the algorithm works fine to succeed in getting the right output.

 

Sequential Ensemble or Boosting mainly pays more focus on predictions that have more errors by preceding weak rules.

 

Types of Boosting

Now it is evident that the answer to weaker models is by combining them to form a stronger and better one. There are different approaches for doing the same and the four most common ways will be discussed briefly in the coming section.

 

  1. Adaptive Boosting or AdaBoost
  2. XGBoosting
  3. Random Forest
  4. Gradient Boosting

 

AdaBoosting: In Adaptive Boosting (or AdaBoosting), equal weights are allotted to all or any predictions. If the prediction is wrong, then it gives higher weights to the wrong one and this process continues till accuracy is achieved. We may use this algorithm for regression or classification problems.

 

XGBoosting: eXtreme Gradient Boosting (or XGBoosting) was given by Tianqi Chen and comes under DMLC (Distributed Machine Learning Community). Here the data is analyzed sequentially and is completed with an increased speed and efficiency of the entire process. It highly boosts the performance of the models. It is capable of creating decision trees parallelly.

 

Random Forest: In the case of Random Forest as well, multiple trees are generated. But this time, they are more complicated and by the voting of the majority weak entities, the stronger ones are being formed. It helps in reducing the variance of the model.

 

Gradient Boosting: In Gradient Boosting, after each iteration, the present learner is more effective than the previous one. Thus, each new model reduces the error and provides a more accurate result set.

 

Benefits of Boosting

The process of boosting is often utilized in great ways to make the best out of the models. From our discussions till now, it is evident that we are ready to make a more complex model with greater accuracy through Boosting. It utilizes the intelligence of the machines to a far better extent as we are learning from our errors. Thus the models are more robust and precise against bias.

 

Applications of Boosting

Boosting is being widely utilized in resolving problems associated with AI. In Self-Driving Vehicles, there is a need to take timely decisions which are not just quick but accurate as well. There is no use in recognizing a stop sign once you have already crashed into it. Thus SDVs need highly accurate decisions within the least possible time and are an excellent example of boosting.

 

Computerized Image Processing needs a system to acknowledge a picture through the computer. This may include picking out the dimensions of the thing, its boundaries, what the thing exactly is, its placement, etc.

 

Conclusion

There are tons of scopes to use Boosting and receiving models of higher dimensions with ease. The process of Boosting has a lot to contribute to in today’s world full of AI, Machine Learning, and Big Data. These are the technologies that are here to remain and a process like boosting that supports them is extremely essential.

 

You can also know about : Machine Learning: How to build a scalable machine structure