In this post, we will try to implement gradient descent approach to solve logistic regression problem. Please note this is mainly for educational purposes and the aim here is to learn. If you want to “just apply” logistic regression in Julia, please check out this one. Let’s start with basic background. Logistic regression is a mathematical model serving to predict binary outcomes (here we consider binary case) based on a set of predictors. A binary outcome (meaning outcome with two possible values, like physiological gender, spam/not spam email, or fraud/legitimate transaction) is very ...

As someone experienced in R, I naturally look for data.frame-like structures in Julia to load a CSV file into. Luckily, it is present and seems to work pretty well. You need to install a package called DataFrames to operate on R-like dataframes: Pkg.add("DataFrames") and load it after installation: using DataFrames; The whole documentation is available here. For now, we will try to load a simple CSV file and play with it. You can use the iris dataset. It is a toy dataset meant for various machine learning tasks. Let’s download it and read it into a variable called iris: iris = readta...