What is Machine Learning With R?

Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand were the creators of R, which is now a widely used language for Machine learning. They created it for the application of S programming language in 1993 and the open source project was set up in 1997.

These two men started it as an experiment to bring into play a statistical test bed in Lisp using programming language providing in S. They eventually realized that they had created something that
exceeded S. R turned out to be the best technology for statistical programming and applied machine
learning.

Machine learning with R
Machine learning with R

While going through this post you will learn:-

  • What is R?
  • It is featured.
  • Benefits of R.
  • Difficulties using R.
  • Who is using R?

What is R?

  • It is a computer language used to write a program and it is a variant of Lisp.
  • You can consider it as an open source environment for statistical programming and visualization.
  • R can disintegrate and run scripts (R program) that are types/loaded from the .R extension.
  • Preparing models that can be queried and updates are also performed by it.
  • It creates and displays graphics on the screen.

Features of R

  • R is used to analyze, plot and build a statistical model for data and is considered an ultimate
    program for one-off analyses prototyping and academic work.
  • On the other hand, it is not well suited for building models to be arranged in scalable or
    operational environment.

Benefits of R

  • R is an open and a free source for all. Anyone can download it and use it anytime.
  • It charges you nothing.
  • It is easy to use.
  • The user can read the source code and learn from it.
  • The user can modify it as per his/her use.
  • The attractive feature of R is its package.
  • It has very powerful algorithms put into service.
  • The users have direct access to it, making them it user-friendly.
  • As read above, R is inspired by propriety language S, hence it is much better for working in
    vectors, matrices and data frame.

Difficulties using R.

  • The main difficulties faced using R are-
  • The algorithm implemented in R has its own naming conventions and parameters. Some make
    predictions but even the result of the standard names can vary due to the complexity.
  • This requires the thorough reading of the documentation in each package that the user uses.
  • The built-in help is not so quite built in and requires help from the web.
  • It is not built for use with streaming or big data or working across multiple machines.

Who is Using R?

Despite these flaws, one cannot ignore the strong benefits that R holds.

  • Many renowned companies such as Oracle, IBM, Mathematica, MATLAB, SPSS, SAS, and others
    provide integration with R and their platforms.
  • Commercial companies too support R.
  • R has been considered as the most competent platform for a successful practicing data scientist.
 

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