Bagging and Boosting

                                                                      Bagging and Boosting

Machine Learning

As you must be already aware that Machine Learning is the branch of Artificial Intelligence (AI) which deals with developing models to predict the results for real-world events as accurately as possible. Various techniques are employed to make a Machine Learning algorithm that is capable enough to allow software applications to predict outcomes with high accuracy without any explicit programming. Historical data are fed to the Machine Learning algos as input to make future predictions by analyzing the trends or patterns. Supervised, unsupervised, semi-supervised, and reinforcement learning are the four basic approaches that can be opted for, depending upon the type of data to be predicted. 

Statistics in Machine Learning

Statistics plays a key role in this interdisciplinary field of Machine Learning that finds its major contribution in training the data to make predictions. Making a Machine Learning model through algorithms is substantially governed by the basics of Statistics. From framing the problem to model predictions, everything involves Statistics to draw inferences from a colossal pool of data as per necessity. In short, the deployment of statistical methods aid in speculating answers to the questions about the data thereby enabling the implementation of Machine Learning. Learn Statistics for Machine Learning without any charges here. 

Ensemble Methods in Machine Learning

In Machine Learning, one of the most useful techniques to have knowledge of is Ensemble Methods Learning. The elementary idea behind the concept of Ensemble Learning is to train different models for the same problem and combine them to improve the results. Here comes the notion of weak and strong/ robust learners or models. Ensemble Methods suggest that a right combination of weak learners or models leads to the development of a strong model or learner with arbitrarily higher accuracy. Ensembles help in cutting down the noise, bias, and variance factors that lead to prediction errors. From here on, we can categorize Ensemble methods into three types – Bagging, boosting, and Stacking. Whenever we apply to bag or boosting to any given dataset, we start with choosing a base learner algorithm.

Single Weak Learner

The quantities of data, distribution hypothesis, the dimensionality of the space, etc. are the deterministic factors in choosing a base model. The most ideal base model is supposed to have the lowest variance and bias. A single base learner might be enough for very simple problems to solve the problem. Still, it is not easy to build a model where the underlying complexity of the data increases. We need an appropriate number of degrees of freedom related to our model for being able to solve a problem. Since the performance of a single base model is very poor, it is known as a weak learner, and we require tweaking our approach a bit at this point. As per the theory of ensemble learning, we combine several of these single weak models for attaining higher accuracy and stability in solving a complex problem.

Bootstrapping

We can understand the variance and bias with the data set through random sampling with replacement. This sampling technique in which we create subsets with replacement from the original dataset is known as Bootstrapping. Bootstrap provides a more clear understanding of the given dataset’s mean and standard deviation as the probability of selection for each example in the dataset is the same. 

What is Bagging?

Bootstrap Aggregating, often termed as Bagging, is used in statistical classification and regression to increase the stability and accuracy of a Machine Learning Model. This ML ensemble meta-algorithm follows the model averaging approach by reducing the variance and consequently avoiding overfitting. Its application can be commonly found with decision tree methods with higher variances. Here, each base model created on a subset formerly originated from the source data set is trained in parallel and independent of each other on the same training set. The predictions from these multiple models are merged to obtain the final predictions.

What is Boosting?

Boosting is another ensemble modeling technique that enhances the stability and accuracy of a Machine Learning model by building a robust model from several weak models. Here multiple models are created, one after the other. Each next consecutive model is based on the previous model. The first step is to create a base model from the given training data set, generating predictions. The next model, which is built, bases its pillars on the failures of the first one. The failures of the second model are cut down in the third one and so on. Series of models keep on adding in the same way until the final correct prediction is obtained from the training data. 

What is Stacking?

The stacking technique of ensemble learning is concerned with merging heterogeneous weak learners. The only difference is that there is no such empirical formula for the weight function in stacking, unlike boosting.  Here each meta-model is trained in parallel and afterward combined to get a more accurate model based on heterogeneous weak model predictions. The weight is computed by introducing a meta-level and using another approach to estimate the input along with the output of every model. Meta-learning is applied to each different weak learner. It can have bagged as well as boosted models as weak learners. Although interpreting stack models can be difficult to a great extent at times, they are the to-working models among the three ensemble methods. 

Bagging v/s Boosting

Similarities

• Both bagging and boosting are types of ensemble methods.

• Both are based on the hypothesis of getting multiple learners from one learner.

• Overall, random sampling is employed to generate different training data sets.

• The final prediction is based on the prediction of meta-weak learners in both cases.

• Both of these techniques work well at minimizing variance and boosting stability.

Differences :

• Bagging combines predictions of the same type, whereas boosting combines predictions of different types.

• Bagging aims at minimizing variance, and on the other hand, boosting aims at increasing predictive force and reducing bias. 

• Each model in bagging is of equal weight, and the performance of the models determines their weight in case of boosting.

• Each model is developed independently from the subsets. Each new model is based on the performance of previous models. 

• Bagging draws multiple training subsets randomly with replacement from the original data set. Boosting takes the misclassified data of previous models as the input for every new subset.

• Bagging is implemented in the cases with high variances (unstable classifier), whereas boosting is implemented in the cases with high bias (stable and simple classifier). 

What is the significance of learning Bagging and Boosting?

The question arises: Why do we need to learn to bag and boost, and even the entire ensemble method learning? So, know that the motive of Machine Learning is always to figure out an ML algorithm that is capable of making accurate predictions. The more accurate the prediction, the better the model is considered. In order to deploy a Machine Learning model and train a given data set to predict with utmost accuracy, the technique of ensemble method learning is made use of. The fundamentals of Machine Learning start deepening from the concept of ensembles. Hence it is essential to know how bagging and boosting works and where to apply them. 

About the ‘Bagging and Boosting’ Course

This course on Bagging and Boosting consists of one hour-stretched video content along with a quiz at the end to test your learning. On clearing the quiz, you earn a course certificate for absolutely free. The Great Learning team has carefully designed the course curriculum to curate comprehensive and fruitful learning of the Bagging and Boosting skills. Introduction to Machine Learning and Basics of Statistics is the potential prerequisite for this course. 

Apart from imparting theoretical knowledge about Bagging and boosting, this course will guide you through a practical demo on Python towards the end. This course kick starts with a lecture on dealing with prediction errors and paves to explain the fundamentals of bagging and boosting to the learners. This online certification course will empower you with a new Machine Learning skill and raise your resume weightage. Enroll in this free course now and start your learning journey right away. Happy learning!