Remove the-curse-of-dimensionality
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The curse of Dimensionality

Domino Data Lab

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.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

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.

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Analyzing Large P Small N Data – Examples from Microbiome

Domino Data Lab

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.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

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.

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So Far, VR-Enabled Data Visuailzation is Nonsense

Perceptual Edge

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.

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Why model calibration matters and how to achieve it

The Unofficial Google Data Science Blog

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.

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