How This Workflow Works
This workflow demonstrates how to build and evaluate a Naive Bayes model that predicts the income category of an adult person. It first splits the data into two parts: one for training the model and one for testing it. Then it prepares the data, trains the model, makes predictions on new data, and finally measures how well the model performs.
Key Features:
- Automatically train a model that classifies data into two groups
- Predict an adult’s income for new records
- Measure model performance using statistical metrics
Step-by-step:
1. Prepare and Split Data:
The workflow starts by cleaning and organizing the data. It handles imbalanced categories by balancing the dataset, fills in missing values, and splits the Adult dataset into a training set and a test set.
2. Train the Model
Next, the workflow trains the Naive Bayes model using the prepared training data. This helps the model learn patterns that link a person’s characteristics (such as age, education, or work information) with their income category.
3. Apply the Model to Test Data:
The trained model then makes income predictions for new, unseen records in the test data.
4. Evaluate Model Performance:
Finally, the workflow compares the model’s predictions with the real income categories in the test set. It calculates statistical scores to evaluate how accurately the model can classify the income category for an adult individual.