Numeric Prediction

For this homework, use the world happiness dataset.

We want to predict the SWL-index attribute (prediction target) from the other predicting attributes: continent, life-expectancy, GDP-per-capita, access-to-education-score. Note that the attribute country identifies each data instance uniquely and as such will be disregarded in our analysis. It is provided just for context.

1. [5 points] Build a regression tree for this dataset in Weka using M5P with the following parameters:
   

   build regression tree: True	unpruned: True	useUnsmoothed: True	default values for the remaining parameters
   

   Record the tree in your report.
   
   
2. [5 points] Build a regression tree for this dataset in Weka using M5P with the following parameters:
   

   build regression tree: True	unpruned: False	useUnsmoothed: True	default values for the remaining parameters
   

   Record the tree in your report [5 points].
   
   
3. [5 points] Build a model tree for this dataset in Weka using M5P with the following parameters:
   

   build regression tree: False	unpruned: True	useUnsmoothed: True	default values for the remaining parameters
   

   Record the tree in your report.
   
   
4. [5 points] Build a model tree for this dataset in Weka using M5P with the following parameters:
   

   build regression tree: False	unpruned: False	useUnsmoothed: True	default values for the remaining parameters
   

   Record the tree in your report [5 points].