Evaluate your R model with MLmetrics
This post will explore using R's MLmetrics to evaluate machine learning models. MLmetrics provides several functions to calculate common metrics for ML models, including AUC, precision, recall, accuracy, etc. Building an example model Firstly, we need to build a model to use as an example. For this post, we'll be using a dataset on pulsar stars from Kaggle. Let's save the file as "pulsar_stars.csv". Each record in the file represents a pulsar star candidate. The goal will be to predict if a record is a pulsar star based upon the attributes available. To get started, let's load the packages we'll need and read in our dataset. [code lang="R"] library(MLmetrics) library(dplyr) stars = read.csv("pulsar_stars.csv") [/code] Next, let's split our data into train vs. test. We'll do a standard 70/30 split here.…