Scikit-Learn vs mlr for Machine Learning

How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.

Scikit-Learn is known for its easily understandable API for Python users, and MLR became an alternative to the popular Caret package with a larger suite of available algorithms and an easy way of tuning hyperparameters. These two packages are somewhat in competition due to the debate where many people involved in analytics turn to Python for machine learning and R for statistical analysis.

One of the reasons for a preference in using Python could be that current R packages for machine learning are provided via other packages that contain the algorithm. The packages are called through MLR but still require extra installation. Even external feature selection libraries are needed, and they will have other external dependencies that need to be satisfied as well.

Scikit-Learn is dubbed as a unified API to a number of machine learning algorithms that do not require the user to call any more libraries.

This by no means discredits R. R is still a major component in the data science world regardless of what an online poll would say. Anyone with a background in Statistics and or Mathematics will know why you should use R (regardless of whether they use it themselves they recognize the appeal).

Now we will take a look at how a user would go through a typical machine learning workflow. We will proceed with Logistic Regression in Scikit-Learn and Decision Tree in MLR.

In either package, there is a process to follow when tuning hyperparameters. You first need to specify which parameters you want to change and the space of those parameters. Then conduct either a grid search or random search to find the best combination of parameter estimates that give you the best outcome (i.e., either minimize error or maximize accuracy).