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You can browse the example gallery to see what you can do with seaborn, and then check out the tutorial and API reference to find out how. To see the code or report a bug, please visit the GitHub repository. General support questions are most at home on stackoverflow or discourse, which have dedicated channels for seaborn.
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add footnote here. Logit model: marginal effects. Marginal effects show the change in probability when the predictor or independent variable increases by one unit. For continuous variables this represents the instantaneous change given that the 'unit' may be very small.
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A dashboard to integrate gadgets and present data output from the gadgets in an integrated user interface. The gadgets dynamically collect information about an advertisement or an ad campaign associated with the advertisement as the information is collected from various sources during the ad campaign, each of some of the gadgets processing collected information and outputting the processed ...
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A basic active learning example with marginal, uncertainty, and random sampling. - active_learning.py
The graph above shows the force exerted by a spring as a function of the length of the spring
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