Debriefing Classification Predictive Model Results

What does Predictive Power measure? : It's your main measure of predictive model accuracy. The closer its value is to 100%, the more confident you can be when you apply the predictive model to obtain predictions. You can improve this measure by adding more variables.

What is Prediction Confidence?: It's your predictive model's ability to achieve the same degree of accuracy when you apply it to a new dataset that has the same characteristics as the training dataset. If it's greater than or equal to 95%, you can consider it a robust predictive model. If it's less than 95%, then you need to improve it, for example, add new rows to your dataset.

Which influencers have the highest impact on the target? Check how the top five influencers impact on the target. For more information, refer to Influencer Contributions.

Which group of categories has the most influence on the target? In Influencer Contributions, you can analyze the influence of different categories of an influencer on the target. For more information, refer to Category Influence, Grouped Category Influence and Grouped Category Statistics.

Using the Confusion Matrix: It's the only way to assess the model performance in detail, using standard metrics such as specificity. It allows you to quicky see the actual correctly detected cases and the false detected cases.

You can use the Profit Simulation tab to estimate the expected profit, based on costs and profits associated with the predicted positive and actual positive targets.

For more information, refer to Confusion Matrix, The Profit Simulation.

Can I see any errors in my predictive model? Is my predictive model producing accurate predictions? Use a large panel of performance curves in the Performance Curves tab, to compare your predictive model to a random model and a hypothetical perfect predictive model: