Boosted Model Example

Boosted Model Example

Problem Statement

Use the XGBoost model to predict the values for gear based on the other variables. The data for the exercise is shown in the Data tab.

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 Boosted Model.


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 model used is AdaBoost Classification Trees (M1).
  • Accuracy: 88.67%, which indicates a good classification performance.
  • The model was trained using Bootstrapping (Boost) as the training method.
  • The optimization graph suggests the model's tuning process to find the best hyperparameters.
  • The selected parameters were optimized randomly.
  • This chart displays the relative importance of different features used in the model.
  • Higher importance values indicate variables that contribute more significantly to the predictions.
  • Data Type: Classification.
  • Features Used: gear, carb, vs, am, disp, qsec.
  • Preferred Measure: Accuracy.
  • Subsampling: None (suggests that all data points were used in training).
  • Model Tuning: Random.
  • Response Variable: gear (the categorical target variable).
  • Training Data: 80% split, 20% missing values handled.
  • Exclusion of Zero Rows: None.
  • Resampling Bootstraps: 84 repetitions.
  • Random seed values for resampling: 23, 29, 29, 29, 29.
  • Different methods evaluated (Breiman, Freund, Zhu, etc.), where the accuracy and Kappa statistics are shown for each method.
  • High Accuracy (88.67%): The model performs well in classifying the response variable.
  • Feature Importance: The model determines which variables contribute the most to predictions.
  • Resampling Stability: Multiple bootstraps ensure robustness.
  • Potential Overfitting Risk: If accuracy is significantly higher than expected, cross-validation should be checked.

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