Can R be used for Machine Learning

Can R be used for Machine Learning?

Yes, R can be used for machine learning. R has a lot of libraries are packages for building machine learning models. R is one of the most commonly used programming languages for machine learning. It is easy to work with and it has a good amount of resources to work with.

Since its design by statisticians Ross Ihaka and Robert Gentleman, R has grown in popularity and usage to become one of the most widely used programming languages in statistics and data science.

R is used by scientists, economists, programmers, and mathematicians for machine learning, data mining, data analysis, bioinformatics, and statistics. R can also be used to develop and build statistical software.

R is used by companies such as Amazon, Google, Flipkart, Firefox, LinkedIn, ANZ, Accenture, Infosys, BOA, HCL Technologies, Cognizant, American Express, Barclays Bank, and many others.

Although R is used by many companies, its popularity has been decreasing over the years. According to Stack Overflow, R is loved by 41.60% of developers versus 58.40% of developers who dreaded it. R was used by 5.07% of developers in 2022 and has reduced to 4.66% of developers in 2022.

R is heavily used in building machine learning models because of its flexibility. Another strength of R is static graphics; it can produce publication-quality graphs that include mathematical symbols. There are many R packages that you can use for machine learning. Some of the popular ones include

  • Classification And Regression Training (CARET),
  • Data Explorer,
  • Mboost
  • Arules
  • RPart
  • Dplyr,
  • MICE Package,
  • Ggplot2,
  • KernLab
  • Nnet
  • MLR3,
  • Xgboost,
  • TidyR,
  • Superml
  • Random Forest,
  • E1071,
  • iGraph,
  • and many others.

R and its libraries are used to implement various techniques such as linear, generalized linear, and nonlinear modeling, classical statistical tests, spatial and time-series analysis, classification, clustering, etc.

Although R can be used for machine learning, there are many other programming languages that are preferred to R, these other programming languages have more resources and a huge community of developers and users than R.

WHAT ARE ALTERNATIVES TO R FOR MACHINE LEARNING?

There are many programming languages that you can use as an alternative to R for machine learning. Here are some of the popular ones.

PYTHON

Python has an easy to understand syntax that makes it easy to write code. Getting started with Python for machine learning is much easier than with R. Python is used a lot by developers for machine learning and artificial intelligence.

According to a 2022 Stack Overflow survey, Python is the third most used programming language by professional developers. It is also one of the most loved programming languages with 67.34% of developers who loved it versus 32.66% of developers who dreaded it.

Python has a lot of resources and libraries that you can use for machine learning and artificial intelligence. Here are some of the popular ones.

TENSORFLOW – An open-source library for developing and training machine learning models. Tensorflow also helps developers to build and deploy machine learning-powered applications. It is used by many companies such as PayPal, Bloomberg, eBay, Dropbox, IBM, Coca-Cola, Google, Airbnb, DeepMind, Uber, Snapchat, Qualcomm, Airbus, Intel, Twitter, and many others.

KERAS – an open-source, high-level, deep learning API developed by Google for implementing artificial neural networks. It can run on Tensorflow, Theano, Microsoft Cognitive Toolkit, and many other platforms.

NUMPY – Numpy is the most popular package for scientific computing with Python. It is used for machine learning, data science, visualization, Array libraries, image processing, signal processing, etc. Numpy also powers many other scientific and machine learning libraries.

Other Python-based machine learning libraries include Scipy, Scikit-Learn, Pytorch, Pandas, Theano, Matplotlib, Nltk

JULIA

Julia is a great language for machine learning, it is a high-level, high-performance dynamic language. Julia is used for Machine Learning, Scientific Computing, Parallel Computing, Data Science, Data Visualization, etc.

Julia has a lot of packages for machine learning, some of the popular ones include MLJ.jl, Flux.jl, Knet.jl, AlphaZero.jl, Turing.jl Metalhead, ObjectDetector, and TextAnalysis.jl. These packages will helpful for Deep Learning, decision trees, clustering, pre-trained models, reinforcement learning algorithms, etc.

For example, the Federal Reserve Bank of New York used Julia to make models of the United States economy (including estimating COVID-19 shocks in 2021), noting that the language made model estimation “about 10 times faster” than its previous MATLAB implementation.

Julia is a very fast and high-performance language compared to many programming languages used in machine learning. Julia is one of the few high-level programming languages in which petaFLOPS computations have been achieved, others being C, C++, and Fortran.

Julia is the 5th most loved programming language, it is used by many companies for machine learning, these companies include Aviva, NASA, Brazilian INPE, Moderna, BlackRock, Climate Modelling Alliance, Google, Microsoft, and many others.

MATLAB

Matlab is one of the most popular numerical computing platforms among engineers and scientists. It is used for Machine Learning, Deep Learning, Algorithm development, Image processing & Computer Vision, Automated Driving Systems, Data Science, and more.

In machine learning, you can use MATLAB to train models, tune parameters, deployment of deep neural networks, and deploy to production or the edge. MATLAB is a great choice for Machine Learning.

It is reported that MATLAB has more than 4 million users. Some of the companies using MATLAB include AMD, DoubleSlash, Broadcom, General Electric, Volvo, Zendesk, Confidential Records Inc, Lucid Motors, Blue Origin, Lockheed Martin, and many others.

C++

C++ is another great and popular option for machine learning and artificial intelligence. You can use libraries such as Caffe, Microsoft Cognitive Toolkit, MLPack, Shark, Gesture Recognition Toolkit (GRT), and many others.

These libraries are helpful for deep learning, artificial neural networks, classification, regression, forecasting, linear and non-linear optimization, algorithm development, and more.

JAVA

Java has a lot of libraries for machine learning. Some of the popular libraries include Weka, Apache Mahout, Deeplearning4j, Mallet, Spark MLlib, JSAT, Encog Machine Learning Framework, JavaML, Massive Online Analysis (MOA), and many others.

These libraries are helpful for deep learning, classification, artificial neural networks, regression, forecasting, clustering, association rules, recommendation, and more.

CONCLUSION

It can be seen that R is popular and widely used for machine learning projects. It has many packages and libraries that are helpful for machine learning projects. However, R’s popularity has been decreasing over the years.

This means that to effectively work on machine learning projects, it is a good idea to use programming languages such as Python, Julia, MATLAB, C++, Java, or C#.