Building simple logistic regression models
The donors dataset contains 93,462 examples of people mailed in a fundraising solicitation for paralyzed military veterans. The donated column is 1 if the person made a donation in response to the mailing and 0 otherwise. This binary outcome will be the dependent variable for the logistic regression model.
The remaining columns are features of the prospective donors that may influence their donation behavior. These are the model's independent variables.
When building a regression model, it is often helpful to form a hypothesis about which independent variables will be predictive of the dependent variable. The bad_address column, which is set to 1 for an invalid mailing address and 0 otherwise, seems like it might reduce the chances of a donation. Similarly, one might suspect that religious interest (interest_religion) and interest in veterans affairs (interest_veterans) would be associated with greater charitable giving.
In this exercise, you will use these three factors to create a simple model of donation behavior. The dataset donors is available for you to use.
Deze oefening maakt deel uit van de cursus
Supervised Learning in R: Classification
Oefeninstructies
- Examine
donorsusing thestr()function. - Count the number of occurrences of each level of the
donatedvariable using thetable()function. - Fit a logistic regression model using the formula interface with the three independent variables described previously.
- Call
glm()with the formula as its first argument and the data frame as thedataargument. - Save the result as
donation_model.
- Call
- Summarize the model object with
summary().
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Examine the dataset to identify potential independent variables
# Explore the dependent variable
# Build the donation model
donation_model <- glm(___,
data = ___, family = "___")
# Summarize the model results