Domino Data Lab

OCTOBER 7, 2020

The Curse of Dimensionality , or Large P, Small N, ((P >> N)) , problem applies to the latter case of lots of variables measured on a relatively few number of samples. Consider a two dimensional space defined by the height and weight of grade school students. Danger of Big Data. Big data is the rage.

Statistics 145

Statistics Testing Predictive Modeling Big Data 145

Domino Data Lab

NOVEMBER 17, 2020

Classical statistics, developed in the 20 th century for small datasets, do not work for data where the number of variables is much larger than the number of samples (Large P Small N, Curse of Dimensionality, or P >> N data). Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings. Introduction.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Even higher skewness has been observed in other domains.

Machine Learning 136

Machine Learning Metrics Data mining Data Science 136

The Unofficial Google Data Science Blog

APRIL 19, 2021

And even if your simpler model is correctly specified, you still might suffer from the curse of dimensionality (see figure 3 in Candes and Sur ). Calibration applies in many applications, and hence the practicing data scientist must understand this useful tool. What is calibration? a sigmoid). This does not require calibration.

Modeling 122

Modeling IT Metrics Testing 122

Perceptual Edge

MAY 14, 2018

I was prompted to write this blog post by a recent article titled “Data visualization in mixed reality can unlock big data’s potential,” by Amir Bozorgzahed. Those who promote it don’t base their claims on actual evidence that it works. Instead, they tend to spout a lot of misinformation about visual perception and cognition.

Slice and Dice 65

Slice and Dice Visualization Big Data Data Processing 65

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 45

Statistics Optimization Modeling Experimentation 45