The Accuracy Paradox
Data Skeptic
Today's episode discusses the accuracy paradox. There are cases when one might prefer a less accurate model because it yields more predictive power or better captures the underlying causal factors describing the outcome variable you are interested in. This is especially relevant in machine learning when trying to predict rare events. We discuss how the accuracy paradox might apply if you were trying to predict the likelihood a person was a bird owNext Episodes
Neuroscience From a Data Scientist's Perspective @ Data Skeptic
📆 2015-11-20 01:00
Bias Variance Tradeoff @ Data Skeptic
📆 2015-11-13 01:00
Big Data Doesnt Exist @ Data Skeptic
📆 2015-11-06 01:00
Covariance and Correlation @ Data Skeptic
📆 2015-10-30 01:00
Bayesian A/B Testing @ Data Skeptic
📆 2015-10-23 02:00