Support Vectors Example

Support Vectors Example

Problem Statement

Use the SVM Analysis Models to predict the values for gear based on the other variables.

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 Support Vectors.

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 SVM model achieved an accuracy of 89.65%, indicating a strong predictive performance.
  • The performance metric suggests that the model is effective in classifying the given data.
  • The hyperparameter tuning graph shows that the cost parameter affects accuracy.
  • The best-performing model used cost = 0.25 and L2 loss for regularization.
  • The Variable Importance Plot highlights the most influential features in the model.
  • Certain features like ‘gear’ and ‘disp’ seem to have higher importance in classification.
  • The model was trained on 23 samples with 29 rows, out of which 6 had missing values.
  • The response variable was ‘gear’, and the model considered multiple predictors.
  • The model employed an L2 Regularized Support Vector Machine with a Linear Kernel.
  • This regularization method helps control overfitting by penalizing large coefficients.
  • The final model was chosen based on the highest accuracy during tuning.
  • The selected parameters were cost = 0.25 and L2 loss, ensuring a balance between bias and variance.

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