May 2019
Intermediate to advanced
664 pages
15h 41m
English
Content preview from Advanced Machine Learning with R
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
Start your free trial
Data creation
In this section, I'll discuss how I created the dataset used for this chapter and provide insight into the features and the class labels we'll endeavor to predict. The data is available on GitHub at https://github.com/PacktPublishing/Advanced-Machine-Learning-with-R/blob/master/Data/sim_df.csv:
- Let's get our libraries and data loaded:
> library(magrittr)> install.packages("glmnet")> install.packages("caret")> install.packages("classifierplots")> install.packages("DataExplorer")> install.packages("InformationValue")> install.packages("Metrics")> install.packages("ROCR")> install.packages("tidyverse")> options(scipen=999) > sim_df <- readr::read_csv('sim_df.csv')
The dataframe is 10,000 observations of 17 variables, consisting ...