The Data Visualisation Catalogue
MAY 6, 2024
Box-Percentile Plots display the same summary statistics as regular Box Plots (median, quartiles, minimum, and maximum), but instead use line markers on a density/distribution shape to indicate their location. i17 Box-percentile plots of height-for-age (HAZ) by country; 2009-10. The Box-Percentile Plot, Warren W. Esty and Jeffrey D.
Smarten
JUNE 17, 2022
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Domino Data Lab
JUNE 8, 2021
The intuition here is that a decision boundary that leaves a wider margin between the classes generalises better, which leads us to the key property of support vector machines — they construct a hyperplane in a such a way that the margin of separation between the two classes is maximised (Haykin, 2009). Derivation of a Linear SVM. Fisher, R.
Perceptual Edge
FEBRUARY 22, 2021
On April 15, 2021, my book Now You See It (2009) will become available in its second edition with the revised subtitle An Introduction to Visual Data Sensemaking. Now You See It: An Introduction to Visual Data Sensemaking. Now You See It teaches the concepts, principles, and practices of visual data sensemaking.
The Unofficial Google Data Science Blog
JULY 11, 2017
SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.
Domino Data Lab
MAY 8, 2019
On the other hand, as Lipton emphasized, while the tooling produces interesting visualizations, visualizations do not imply interpretation. ML model interpretability and data visualization. From my experiences leading data teams, when a business is facing difficult challenges, data visualizations can help or hurt.
The Unofficial Google Data Science Blog
MARCH 13, 2017
Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. A note on visualization The most convenient way to inspect our feature importances (attributions) is to visualize them.
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