Build and Evaluate A Logistic Regression Classifier

This article is part of a R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.

Logistic regression is a simple, yet powerful classification model. In this tutorial, learn how to build a predictive classifier that classifies the age of a vehicle. Then use ggplot to tell the story!

Here are the links to get set up. ????

• Get the Code
• YouTube Tutorial

The Story

In this analysis we learn that newer vehicles are MORE EFFICIENT, and we’ll make a data visualization that tells the story.

How did we make this plot?

1. Our logistic regression classifier modeled the data
2. We used VIP to find the most important features
3. We visualized with ggplot ????

Making a Logistic Regression Classifier

Logistic regression is a must-know tool in your data science arsenal.

• Logistic Regression is easy to explain
• The classifier has no tuning parameters (no knobs that need adjusted)

Simply split our dataset, train on the training set, evaluate on the testing set.

Folks, it’s that simple. ????

Evaluating Our Classification Model

Question: How do we know our if our model is good?
Answer: Area Under the Curve (AUC)!

About AUC:

• Simple measure.
• We want greater than 0.5.
• Closer to 1.0, the better our model is.
• Bonus: ROC Plot – A way to visualize the AUC.

Telling the Story

What can we do with a Logistic Regression Classifier? Let’s develop a story to communicate our insight!

1. First, find the most important features (predictors) using vip().

2. Next, use ggplot() to make a visualization that focuses on the top features:

• HWY: The highway fuel economy (miles per gallon)
• CLASS: The Vehicle Class (e.g. pickup, subcompact, SUV)

What did we learn using Logistic Regression?

It’s clear now:

• Vehicles have become more efficient over time.
• Highway fuel economy has gone up for every single class of vehicle.

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