Which dummy variable to drop

Create a simple categorical variable:. Then convert the categorical variable to dummy variables:. You can see that pandas has one-hot encoded our two tree species into two columns.

Now, we simply know whether a tree is pine or not pine. For a more complex example, consider the following randomly created dataset:. Course Recommendations. Deep Learning Specialization — Coursera A series of five courses dedicated to the creation and tuning of deep neural networks, convolutional neural networks, and sequence models.

Take the internet's best data science courses Learn More. Get updates in your inbox Join over 7, data science learners. Fatih Karabiber Ph. Back to blog index. Load Comments.

Often, people will set aside the category which is most populated or one which acts as a natural reference point for the other categories.

Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. What is the general guideline for dropping dummy variables in a regression model? Asked 3 years, 6 months ago. Active 3 years, 6 months ago. Viewed 5k times. Improve this question. Jane Wayne Jane Wayne 1, 2 2 gold badges 14 14 silver badges 23 23 bronze badges.

So whatever y represents, males have an increase of 0. Add a comment. Active Oldest Votes. For any such categorical variable, when you use dummy coding what you are essentially saying is: - I will set aside one of the categories of the variable and treat it as the reference level; - I will compare the mean value of y corresponding to each of the remaining categories against the mean value of y corresponding to the reference level controlling for everything else in the model.

How did we specify the effects of type of job and ethnicity in our model? Thanks a ton. Hello, i've been watching a free course on Itroduction to Computational Thinking and Data Science by MIT OCW and I found an interesting situation in which the professor made a logistic regression model to predict wheter a passenger died or not in the Titanic. Wasn't he supposed to fall in the dummy variables trap?

Ricardo - Not necessarily. There are a lot of ways to code variables that avoid the dummy trap. In the titanic example it sounds like the professor was using was is called the cell means model. You can include all three cabin classes in the model as long as there is not also an intercept.

In this case the coefficients for the model will represent the average survival rate for each of the cabin classes. There is no intercept and thus there is no base line reference from which to difference.

Using dummy coding with an intercept the omitted cabin class is the reference and each coefficient represents the difference between reference category and the cabin class. There is also no base case so each coefficient is just the predicted value for the dependent variable for that cabin class. In linear regression this is just the mean for each cabin class.

Notice there are still three rows and three columns even though we dropped the last column to avoid the dummy trap. In this setting Cabin Class 3 serves as the reference level since it is the variable that is dropped. In this case the interpretation of the coefficients are: The intercept is the prediction for the dependent viable assuming that the observation is not in C1 and not in C2.