Evaluation of my GBM ML model

Machine Learning and Modeling
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1

Hi,

I trained my model using GBM algo from Caret package. I fine tuned it using Grid search. Then I evaluated my model using confusion matrix and ROC value. Below is the numbers for evaluation -

  1. For Training Data - ROC - 0.94, Accuracy - 87.76%, Sensitivity- 88.67%, Specificity - 85.55%
  2. For Test Data - ROC - 0.94, Accuracy - 88.09%, Sensitivity- 87.64%, Specificity - 85.59%

I wanted to know that, whether my model is good or not, if not then what all parameters should I focus more for getting the generalized model?

2

"Good" is really defined by the problem that you are working on. An AUC of 0.94 would be envied by many people who model data though.

3

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