Category: R

The folks at Data Sharkie walk us through random number generation in R:

Why is random numbers generation important and where is it used?

Random numbers generations have application in various fields like statistical sampling, simulation, test designs, and so on. Generally, when a data scientist is in need of a set of random numbers, they will have in mind

R programming language allows users to generate random distributed numbers with a set of built-in functions: runif(), rnorm(), rbinom().

Read on to generate random numbers across two separate distributions.

Michaelino Mervisiano takes us through the types of visuals we can create to understand a single variable in R:

How to create a histogram in R? And what information that we can get from histogram?
Histogram shows a frequency distribution. It is a great graph for showing the mode, the spread, and the symmetry (skewness) of your data. Here is a histogram of 1,000 random points drawn from a normal distribution with a mean of 2.5

Of course I don’t like option number 4 and would replace it with something else (column/bar charts, Cleveland dot plots, or stacked column/bar depending on what you’re trying to observe). But this is a good way of thinking about how you can visualize a variable.

Julia Silge gives us an idea of how to tune random forest hyperparameters in R:

Our modeling goal here is to predict the legal status of the trees in San Francisco in the #TidyTuesday dataset. This isn’t this week’s dataset, but it’s one I have been wanting to return to. Because it seems almost wrong not to, we’ll be using a random forest model! 🌳

Let’s build a model to predict which trees are maintained by the San Francisco Department of Public Works and which are not. We can use parse_number() to get a rough estimate of the size of the plot from the plot_size column. Instead of trying any imputation, we will just keep observations with no NA values.

Click through to some data exploration, the initial model, and a process for using Grid Search with the caret package.

Abderrahim Lyoubi-Idrissi takes us through a Bayesian approach to tune hyperparameters:

In contrast to the model parameters, which are discovered by the learning algorithm of the ML model, the so called Hyperparameter(HP) are not learned during the modeling process, but specified prior to training.

Hyperparameter tuning is the task of finding optimal hyperparameter(s) for a learning algorithm for a specific data set and at the end of the day to improve the model performance.

Abderrahim contrasts two different methods here: Grid Search and Bayesian Optimization. Definitely an interesting read if you develop data science models.