Machine Learning with R: Everything You Need to Know in 2020

R is a powerful programming language that has a unique software environment that is available for free usage of statistical computing and graphics. This capability makes it amongst the most widely used languages for not only statistical computing but data analysis as well.

The development of R happened in the early 90s and since then its user interface has undergone several improvements. It was initially a rudimentary text editor that turned into interactive R Studio a little later. Its most recent expedition with Jupyter Notebooks has been seen as a significant step in its nearly three-decade-long journey. 

The improvements that have been made to R over the years have been due to the contributions made by the community of R users that are spread across the length and breadth of this world. Many powerful packages have continuously been added to this language that has made it such a popular language amongst worldwide machine learning and data science communities. Some of the packages include rpart, readr, MICE, caret, and others. We will discuss how a few of these packages play an important role in the implementation of machine learning in R.

Check out: 6 Interesting R Project Ideas For Beginners

Overview of Machine Learning

As you would already know, machine learning algorithms are broadly classified into two types – supervised machine learning (SML) algorithms and unsupervised machine learning (UML) algorithms. Supervised machine learning algorithms are those that are presented using inputs with labels, which make an indication of the desired output. SML algorithms are further divided into regression algorithms that have a numerical output and classification algorithms that have a categorical output. On the other hand, unsupervised learning algorithms are those that don’t have labeled inputs. The focus here is to detect the data structure in unlabelled input. 

You will also come across semi-supervised learning algorithms and reinforcement learning algorithms as you deepen your study of machine learning and the problems it can be used to solve.

Read more: Everything You Should Know About Unsupervised Learning

Is R right for machine learning?

A lot of people think that R is only good for statistical computing. However, they soon realize their mistake. There are several provisions in R that can make the implementation of machine learning algorithms much simpler and faster. 

R is amongst the most preferred languages for data science projects. It comes with visualization features that you can associate with other languages. These features help in exploring the data in the right manner before it is sent to an automated learning algorithm for further application and at the same time, evaluating learning algorithm results. 

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Packages to implement machine learning algorithms in R

1. Multivariate imputation by chained equations or MICE package is mostly used to implement a method that is capable enough to handle missing data. It creates multiple replacement values related to missing data. In this method, there is a separate model that is attributed or assigned to every incomplete or missing variable.

You can now easily associate it with Fully Conditional Specification. MICE can be used to assign a mix of binary, continuous, ordered categorical, and unordered categorical data. It can attribute two-level data in continuous form and use passive attribution to maintain the required consistency. Attribution quality is examined by implementing several diagnostic plots.

2. rpart package is used to perform recursive portioning in decision trees, classification, and regression algorithms. This procedure is carried out in two simple steps. The result of this procedure is a binary tree. Plotting of results, which are achieved with the help of rpart, is done by calling the plot function. rpart can be used to perform classification as well as regression. It helps in understanding the variance that is using the independent variables to impact the dependent ones.

3. The random forest package or approach sees the creation of several decision trees. Each one of these trees is fed with observations. The final output is determined by the outcome that most commonly appears with different observations. 

4. The caret package is short for classification and regression training. It is used to make predictive modeling much simpler than it usually is. You can use caret to conduct controlled experiments to identify optimal parameters. A few tools that you will have access to when using this package include model tuning, data pre-processing, feature selection, and data splitting amongst others. 

5. You can use the e1071 package to implement Support Vector Machines (SVM), Naïve Bayes, Bagged Clustering, and Fourier Transform amongst other machine learning algorithms. SVM is one of e1071’s best features. It allows users to work on data that can’t be separated on the dimension that is made available to them. Users need the dimensions to carry out regression or classification on dimensions that are higher than the given ones. 

6. The nnet package is an add-on of the R language that prepares the groundwork for creating neural network classifiers. You can create just a single layer of nodes with this package. It simplifies all the steps that are a part of the neural network creation process, including data preparation, evaluation of the model’s accuracy, and making predictions. 

Know more: Best Programming Languages for Machine Learning

Conclusion

In this blog, we discussed the relationship between R and machine learning and how this programming language can be used to implement several machine learning algorithms. 

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