👉 Lift computing is an innovative approach to measuring the performance of machine learning models that focuses on comparing their ability to solve a specific task, rather than relying solely on traditional metrics like accuracy or F1 score. It computes the "lift" of a model by quantifying how much better it performs on a target task compared to a baseline model, typically a random classifier or a simple model. Lift is calculated as the ratio of the lift (the improvement in performance) to the probability of random guessing. This method is particularly useful when dealing with imbalanced datasets or tasks where the distribution of classes is skewed, as it provides a more nuanced understanding of a model's effectiveness in real-world scenarios. By emphasizing the practical utility and relative performance improvements, lift computing helps researchers and practitioners identify models that not only achieve high accuracy but also deliver meaningful gains in specific applications.
