In a Cumulative Lift chart, what do high values suggest about the model's performance?

High values in a Cumulative Lift chart indicate that the model is performing well. The Cumulative Lift chart is a tool used to evaluate the effectiveness of a predictive model by comparing the model's predictions against random or baseline performance. The Cumulative Lift reflects the increase in the number of positive responses captured by the model compared to what would be expected if the model made random selections.

When the values on the Cumulative Lift chart are high, it means the model is successfully identifying a larger proportion of true positives within the top percentage of predicted probabilities. This suggests that the model is effective in distinguishing between the positive and negative classes, enabling better decision-making based on the model predictions. High lift values demonstrate that a greater-than-expected number of positive cases are being targeted based on the model's outputs, highlighting its utility in practical applications.