Showing posts from December, 2021

Assessing the quality of predictive models in Oracle Analytics using Lift & Gain

Anyone who has done some machine learning modelling in Oracle Analytics, might have been missing a bit more of quality metrics to better assess the quality of trained models. By default any generated classification model would enable users to evaluate trained models by reviewing Quality metrics such as Precision , Recall , Accuracy , F1 and False Positive Rate . When presenting Machine Learning support within Oracle Analytics, the question about calculating  Gain and Lift are often asked. Until now, Oracle Analytics didn’t support this functionality and this has changed in the recent release of Oracle Analytics 6.3 . Lift and Gain Analysis In order to measure how the prediction model is better then not using a prediction model at all, Lift and Gain Analysis is used. Using other words, with Lift and Gain analysis we can find out what is the benefit of applying predictions based on a prediction model to the business. In Oracle Analytics, Lift and Gain Analysis is performed by visual