From the course: Data Science Foundations: Data Assessment for Predictive Modeling
Unlock the full course today
Join today to access over 22,400 courses taught by industry experts or purchase this course individually.
Advice for weak predictors
From the course: Data Science Foundations: Data Assessment for Predictive Modeling
Advice for weak predictors
- [Instructor] Okay, we're going to continue with the same dataset and the same workflow but turn our attention to weak predictors. So I'll close this. So when we're looking at predictors, we're going to have to do more detective work. The way most people behave is they treat the data understanding phase as a kind of screening phase and they get rid of variables left and right. We don't want to do that. I want to encourage a different approach. What you want to do is when there's a variable that has problems, dig deep and see if you can't save it. See if there are interesting relationships there. So for instance, the variable that comes to mind for having problems would be variables like capital-gain and capital-loss. We'll look at capital-gain. The reason is there's so many zeros. Let's run this and talk about it. That large number of zeros isn't necessarily going to bring the variants and standard deviation way…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Contents
-
-
-
-
-
-
-
-
-
-
How to utilize an SME's time effectively2m 8s
-
Techniques for working with the top predictors4m 19s
-
Advice for weak predictors6m 4s
-
Tips and tricks when searching for quirks in your data4m 46s
-
Learning when to discard rows2m 5s
-
Introducing ggplot21m 44s
-
Orientating to R's ggplot2 for powerful multivariate data visualizations5m 52s
-
Challenge: Producing multivariate visualizations for case study 11m 12s
-
Solution: Producing multivariate visualizations for case study 12m 31s
-
-
-
-
-
-