Machine learning: Python vs. R

Machine learning (ML) is one of the fastest growing branches of artificial intelligence in modern times. It allows a system to learn from past experiences and generate a more appropriate response, just as a human mind would. The backbone of ML is effective data analysis.

R and Python are the most popular languages used for machine learning. Both open source languages provide a huge repertoire of statistical and predictive tools. However, they take very different approaches to data analytics. Let’s dive into the strengths and weaknesses of the two ML giants and decide which would be the best fit based on individual requirements.

The nature of the languages

Python is a general-purpose language with a huge set of data science libraries. For a flexible and extensive ML project, Python would be a better fit.

R focuses more on the statistical analysis and visualization of data. While it is not a general purpose language like its rival, it boasts a larger variety of statistical libraries.

Optimization and speed

Being a general-purpose high level language, Python is not always the best option in terms of speed.

R, on the other hand, is a simpler language which can even perform complex computations really fast. So, R takes the cake in this round.

Machine learning packages

Python has some of the most popular ML packages in today’s world. Scikit-learn, one such package, presents users with several fundamental tools for creating neural networks and analyzing data.

R has its own packages for solving ML problems, Caret being a very popular one. The Nnet library is a suitable platform for modeling neural networks.

Although this is a close one, Python comes out on top due to the robust and powerful libraries it provides.

Data analysis packages

Python is a great option for analyzing and modeling data. With packages like NumPy and pandas, there are so many options to explore.

In comparison, R still seems like a better choice, since the language was made with the sole purpose of data analysis. This can play a vital role in machine learning.

Even though R is less readable than Python, it showcases detailed visualizations and diverse libraries which make it worth the steep learning curve.

Integration with other languages

Python has a much better integration rate with other languages. It even supports R functionality through the RPy2 package!

Due to R’s difficult syntax, integration can be a challenge, especially for new users.

Consistency

Beyond basic functionality, R relies heavily on third-party code for several packages. This creates an inconsistency between the available algorithms.

Python is much more flexible and well-rounded in this regard. The approach of creating large packages containing smaller functionalities ensures that the code is uniform across the platform. It also has better technical and community support.

Conclusion