Machine Learning Times
Machine Learning Times

Mike Thurber

Measuring Invisible Treatment Effects with Uplift Analysis

  Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk

Uplift Modeling: Making Predictive Models Actionable

 Predictive models typically estimate the likelihood of future events, such as whether it will rain tomorrow or which customers are most likely to “churn” by cancelling their phone contract.  In the case of the weather, we do...