When Benchmarks Are Not Reliable for Model SelectionWe love using benchmarks to select the best model.Oct 23Oct 23
Feature Interaction in Medical Science (and beyond)In data science, we often focus on building models that can predict outcomes based on individual features or variables, such as age…Oct 16Oct 16
Debunking the Power Pose: Why Scientific Rigor MattersThe video has more than 25 million views on YouTube, and we still hear about it from self-help gurus years later.Oct 9Oct 9
Your model predicts well, but is it the whole story? The weather app dilemmaYour weather app may often correctly predict when it will rain, but it consistently reports only a 55% chance of rain.Oct 4Oct 4
A result can be statistically significant but still have little to no practical relevanceThere, I said it!Jul 2Jul 2
A caveat about K-means clustering and many other machine learning models: StochasticityYour amazing data scientist has performed K-means clustering to segment customers, and the clusters made sense and you could label them…Jun 25Jun 25
“Is your AI model fair?” There are 10 ways to answer that.Many people don’t know this, but there are at least 10 ways to answer the question:Jun 20Jun 20
Is scalability a data scientist’s problem or an engineering problem?Should data scientists worry about how their solutions scale, or is it an engineering problem to solve?Jun 11Jun 11
Common Metrics to Evaluate a Model’s FairnessYup! They made the model technically “fair” by making it less accurate equally across all groups!May 28May 28
AS3DM or Agile Scientific Data-Driven Decision Making for the win! but what is it?Only 32 percent of business executives say they create measurable value from data, and just 27 percent report that their data and analytics…May 16May 16