Pros and Cons of Using “R” in ML Explained by Programming Assignment Helpers

The world is rapidly moving towards the implementation of artificial intelligence in every domain of technology. Machine learning is a significant part of AI that makes the system develop traits within itself based on the information given by the user. It requires excellent programming skills to conquer every concept of ML with perfection. Many students in the UK rely on the R programming language to solve machine learning algorithms because they consider it the best to run heavy codes. But, many programming assignment help experts believe the logic that every coin has two sides. With many advantages come disadvantages as well. So, this article will look at the main pros and cons of using the R programming language for machine learning operations. Go through the following points to get relevant information regarding the topic

Pros

1-Best in Statistical Computations- 

It is the most effective advantage of R in machine learning. ML models demand high statistical calculations to teach various traits of a particular scenario or a problem to the system. The solution of different algorithms involving such computations becomes easy by R. The extended library features helps the coder to solve complex calculations in less time and with fewer lines of code.

2-Best for the Mood Prediction- 

In medical field, it is important to predict the impact of the symptoms on the patient and get relevant information to deal with it properly. R is the best language to synthesize that mood prediction algorithm. It is stated by numerous coders around the globe. Thus, R provides support to implement ML in health-care sector

3-Best in Terms of Environment- 

R has a spectacular environment which is helpful for both machine learning calculations and software development. It is the reason many trained developers give while shifting to it from any other language. The Rshiny package of R comes with many extended features that make it the best to use..

Cons

1-Poor Data Handling- 

Machine learning systems require an enormous amount of data which needs to be well-organized to avoid any problem or confusion. R is poor in data handling because it needs the entire data gathered at one particular location. Thus, it is time taking to cope with big-data clusters by using the R programming language. 

2-Poor in Terms of Security- 

The security structure of R has many holes in it. Data submitted to machine learning algorithms need to get secured because a leak can result in more consequential problems. Thus, it is a huge disadvantage of R. Not only in machine learning but, the failure can also result in spoiling the web applications that contain confidential data.

3-Poor in Memory management- 

The codes written in R consume an immense amount of memory. It sinks with the fourth point about data handling. More data requires more space and, the consumption of extra space results in crashes. So, it is a disadvantage of using R in machine learning.

Conclusion- 

Now you are aware of the pros and cons related to the R programming language. Numerous developers use it to compute machine learning procedures. You can take the help of programming assignment help experts to remove the complexities involved with R programming. Hope this article helps you understand the pros and cons of the language. Good Luck!