Boosting in Machine Learning

Machine Learning and Artificial Intelligence are one of the major modern technologies. In Machine Learning, we mainly develop a model by a Machine Learning algorithm which can predict something. In this method, we use boosting technology to improve our model. In this article, we will learn about boosting in details.

What is Boosting?

There are two types of Machine Learning models. First one is strong learner and second one is weak learner. A weak learner Machine Learning model is weak because it cannot predict with high accuracy. For this reason we need strong learner Machine Learning model which can predict with high accuracy and without error. Boosting is a method by which we can convert multiple weak learners into a single strong learner.

Example: Suppose, you have to built one model which can identify the dogs. Now for a weak-learner model, which give us just random prediction will only work on the new cases which are similar to its present data set. Suppose we have trained the model with some images of brown dogs which are tall but when we pass an image of white dog with short size then it may not recognize the animal as dog. Now we can make this weak learner to a strong learner. We have to use multiple models. Suppose we have two model one for colour of dog and another for shape and size of the dog. If we use boosting here then we can make one strong Machine learning model.

How does boosting work?

Let’s understand the method of boosting. Boosting is mainly worked on decision tree algorithms. Below is the list of algorithms used in boosting:

Decision trees: It is a special data structure in Machine Learning. It mainly divides the whole set of the data into small subsets. It continues dividing the set till there is only one left. It may divide by some special features.

Boosting ensemble method: For every model there is work decision tree. Boosting ensembles the weak decision trees sequentially and makes the model stronger. It mainly takes the classification from first decision tree and then goes to next tree. After calculating all the output from weak trees it concludes them as strong output.

Boosting compared to bagging: This two methods are both ensemble method but they are different from their working principle. Boosting trains model one by one sequentially and makes them strong learner. But in bagging method one weak learner is passed through multiple training sets. In this way it converts weak learner into strong learners. Boosting and bagging both are accuracy improvement methods.

Training of models in boosting

There are different steps in boosting. It depends upon the algorithm used in boosting. But there are also some general steps for all boosting algorithms. They are as follows:

Step 1

Firstly, boosting algorithms pass data in first Machine Learning model which is also called as base algorithm. This model predicts the output after taking the sample data sets with equal weights.

Step 2

In second step, boosting algorithm checks the predictions of first model and the change the weight of the data sets. After that it passes the data through next model. The strong models effect the total results mostly.

Step 3

In this way the data set with weights are passed through models.

Step 4

Previous two steps are followed until we get a satisfying output.

What is the importance of boosting?

Boosting is an important method in Machine Learning. The following points mentioned here, are the major importance of boosting

Easy to implement

As we have discussed above it is very easy to understand. It is also simple for implementing. It does not need any pre processing of the data. It can be implemented in ease. In many languages there are libraries available, by which we can implement the boosting algorithms.

Reduction of bias

We have discussed about weak learners which are Machine Learning models that predicts with inaccuracy or bias. It is very common in Machine Learning models. To reduce the bias in Machine Learning model we need the boosting method. It combines the weaker models into strong models.

Computational efficiency

If we have large dataset which have many feature then it is very complex to calculate the prediction. But boosting mainly depends upon important feature. For this reason we can get more efficiency by reducing our dataset.