Question

A) Partition the data to develop a naïve Bayes classification model. Report the accuracy, sensitivity, specificity, and precision rates for the validation data set. Use 60-40 Training-Validation split with 12345 as the random seed when creating the validation column. A community center is launching a campaign to recruit local residents to help maintain a protected nature preserve area that encompasses extensive walking trails, bird watching blinds, wild flowers, and animals. The community center wants to send out a mail invitation to selected residents and invite them to volunteer their time to help but does not have the financial resources to launch a large mailing campaign. As a result, they solicit help from the town mayor to analyze a help www.my data set of 5000 local residents and their past volunteer activity, stored in the Volunteer_Data worksheet. The data include Sex (F/M), Married (Y = married, N = not married), College (1 if college degree, O otherwise), Income (1 if annual income of $50K and above, otherwise), and Volunteer (1 if participated in volunteer activities, O otherwise). They want to use the analysis results to help select potential residents who are likely to accept the invitation to volunteer. Hint: If needed, you may need to change the data type of certain variable(s) from continuous to nominal in JMP.

Answer 1

To develop a naïve Bayes classification model, you need to partition the data into a training set and a validation set. The training set will be used to train the model, while the validation set will be used to evaluate its performance.

Here's how you can proceed:

1. Split the data: Use a 60-40 training-validation split to divide the data set into two parts. The training set will contain 60% of the data, and the validation set will contain the remaining 40%. You can use a random seed of 12345 to ensure consistent results.

2. Preprocess the data: Before training the model, it's important to preprocess the data. Convert any continuous variables to nominal variables, if necessary. For example, you can convert the "Income" variable from a continuous variable to a nominal variable by creating a new variable "Income_Category" with two levels: "Above $50K" and "Below $50K". This will help in the classification process.

3. Train the model: Use the training set to build the naïve Bayes classification model. The model will learn the patterns and relationships between the input variables (Sex, Married, College, Income) and the target variable (Volunteer). Naïve Bayes assumes that the input variables are conditionally independent given the target variable.

4. Evaluate the model: Once the model is trained, use the validation set to evaluate its performance. Calculate the accuracy, sensitivity, specificity, and precision rates for the validation data set.

- Accuracy: It measures the overall correctness of the model's predictions. It is calculated as the number of correct predictions divided by the total number of predictions.
- Sensitivity: Also known as the true positive rate, it measures the proportion of actual positive instances that are correctly identified by the model.
- Specificity: Also known as the true negative rate, it measures the proportion of actual negative instances that are correctly identified by the model.
- Precision: It measures the proportion of true positive predictions out of all positive predictions made by the model.

By reporting these rates, you will have a clear understanding of how well the naïve Bayes classification model is performing in predicting the likelihood of residents accepting the invitation to volunteer.