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In this video, we learn about lift charts, a tool for evaluating classification models by sorting classification results based on the probability of a target class. The process involves organizing predictions and confidence levels into a bar chart, grouping them into deciles. Each group's proportion of true positives is analyzed, illustrating the effectiveness of the model. For example, the first 10% of data had 99 true positives, covering 28% of all true positives in the dataset. This method helps in assessing the model's performance accurately.