How R Programming is Used in Machine Learning?

What is R Programming?

R is an open-source programming language and environment for graphics and statistical computing. It is effectively used as a statistical software and data analysis tool. It is also used in data science by data science professionals. Join R Programming Training in Chennai at FITA Academy to get immense training with the help of real-time experts.

R is a universal programming language that works on Windows, Macintosh, UNIX, and Linux. It is frequently referred to as a different implementation of the S language and environment. It is highly extensible. Here, we will discuss why R Programming is important.

What is Machine Learning?

Machine learning is an element of artificial intelligence (AI) and computer science that uses data and algorithms to replicate how humans learn, gradually improving its accuracy. Machine learning is a critical component of the rapidly expanding field of data science.

How R Programming is Used in Machine Learning?

The R platform is not appropriate for all machine learning projects. Only major projects can use R for exploration and one-off model building.

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Interactive Exploration Environment:

The R interactive environment is excellent for exploring and learning about packages and functions. When you start, you should spend a lot of time in the interactive environment.
The environment is also ideal for investigating a new problem. Not a systematic approach to the problem but rather a series of what-if scenarios.
It’s also great if you want to use a systematic process to quickly create a prototype model without going through the full rigmarole.

Scripts Should be Used for One-Off Models:

In your project, the tasks could be described in a new script that can be documented, updated, and tracked through revision control. R scripts can be run from the command line, shell scripts, or Makefile targets.

Not for Use in Production:

R is not the best choice for creating a production model. R may be the best solution for determining which model to use in production.
The techniques may be cutting-edge, but they may not employ the best software engineering principles or tests or be scalable to the size of datasets you may need to work with.
People are writing R scripts to run operationally, and new services are emerging to support larger datasets.

Conclusion:

So far, we have discussed How R Programming is Used in Machine Learning? So, to have a comprehensive understanding of R programming, you can join R Programming Course in Bangalore and equip yourself with its applications and functions.