This article explains how to programmatically identify and deal with outlier data (it's a follow-up to "Data Prep for Machine Learning: Missing Data"). Suppose you have a data file of loan ...

Outlier is the third journalism service MuckRock has adopted in the past few years, and the two groups’ incumbent leaders Sarah Alvarez and Michael Morisy plan to help address more information needs ...

Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...

Comparing die test results with other die on a wafer helps identify outliers, but combining that data with the exact location of an outlier offers a much deeper understanding of what can go wrong and ...

Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most ...

The first half of the year has seen a domino effect of states passing data privacy laws one after the other. While some have aligned in their way of protecting consumers' privacy, others have taken ...

Last October, Detroit local and property data analyst Alex Alsup published a Substack with an eye-catching headline: “2,400 Former Detroit Homeowners Might Be Able to Recover a Total of $20M in Tax ...

The Data Science Lab Data Prep for Machine Learning: Outliers After previously detailing how to examine data files and how to identify and deal with missing data, Dr. James McCaffrey of Microsoft ...