Classification Models

Here are steps to perform classification analysis using different models in SPSS, including logistic regression, decision trees, and support vector machines:

Logistic Regression:

1. Open SPSS:

• Launch SPSS and open your dataset.

2. Select Analyze:

• Navigate to the “Analyze” menu at the top.

3. Choose Regression:

• Under “Analyze,” select “Regression” and then choose “Binary Logistic.”

4. Specify Variables:

• Move the dependent variable (categorical) into the “Dependent” box.
• Move the independent variables (predictors) into the “Covariates” box.

5. Options (Optional):

• Click on the “Statistics” button for additional statistics.
• Explore other options as needed.

6. Click OK:

• Once you’ve set up your variables and options, click “OK” to run the logistic regression analysis.

7. Interpret Results:

• Examine the output for coefficients, odds ratios, significance levels, and other relevant statistics.

Decision Trees:

1. Open SPSS:

• Launch SPSS and open your dataset.

2. Select Analyze:

• Navigate to the “Analyze” menu at the top.

3. Choose Decision Trees:

• Under “Analyze,” select “Classify” and then choose “Decision Tree.”

4. Specify Variables:

• Move the target variable (categorical) into the “Target” box.
• Move the predictor variables into the “Predictors” box.

5. Options (Optional):

• Click on the “Fields” button to customize variable roles.
• Explore other options as needed.

6. Click OK:

• Once you’ve set up your variables and options, click “OK” to run the decision tree analysis.

7. Interpret Results:

• Examine the output for the decision tree diagram and other relevant information.

Support Vector Machines (SVM):

SPSS doesn’t directly support SVM as of my last knowledge update in January 2022. However, you can use Python or other tools for SVM analysis. If you have Python integrated with your SPSS, you can use the Python extension to run SVM. Alternatively, you can use dedicated machine learning platforms like scikit-learn.

Always remember to interpret the results carefully and consider model performance metrics (e.g., accuracy, precision, recall) when evaluating classification models.