Decision Trees Example

Decision Trees Example

Problem Statement

We have collected data on several different models of cars with respect to several parameters. Create a decision tree model to predict car mileage. Use a conditional tree with a confidence level of 95%.

How to perform analysis

Step 1: Open Sigma Magic
  1. Click on the Sigma Magic button on the Excel toolbar.
  2. Click on the New button to create a new project.
Step 2: Add the analysis template
  1. Click on the Tool Wizard to add the analysis template.
  2. Click on Analytics and then Decision Trees.

Step 3: Specify analysis options
A new worksheet will be added to your workbook. Analysis Setup will be automatically opened, in the setup tab specify the survey results.


Click on Data to specify the data required for this analysis. 


Click on the Train the software will let you pick the options for training the given model. Training is a step where we split the data into groups a train data set and a test data set.  



Click on the Tuning to identifying the best set of hyperparameters that gives the best fit for the given model.



Click the Verify tab to ensure all the inputs are okay and shown in a green checkmark.


Step 4: Generate analysis result
Click OK and then click Compute Outputs to get the final results.



 Interpretation of Results

  • The Conditional Inference Tree model has a Root Mean Squared Error (RMSE) of 3.45, indicating the average deviation of predictions from actual values.
  • The model was trained on 32 samples with 8 predictor variables (disp, drat, hp, qsec, vs, gear, etc.).
  • No preprocessing was applied to the data before training.
  • Bootstrapped resampling with 3 different sample sizes (32, 32, 32) was used to estimate model performance.
  • RMSE values were evaluated for different hyperparameter settings.
  • The optimal model was chosen based on the lowest RMSE, which was found at a mincriterion value of 0.93.
  • A Variable Importance Plot was included, showing which predictors significantly impacted the model.
  • Certain variables, such as hp and wt, played a major role in decision-making.
  • The model successfully analyzed the dataset and provided predictions.
  • The final Conditional Inference Tree consists of 3 terminal nodes, indicating a relatively simple structure.
  • The model can be further improved by refining hyperparameters or using additional preprocessing techniques.

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