Before modeling a dataset, do you remember to check if it seems IID?
Distribution drift and interactions between datapoints (autocorrelation) are common violations of the Independent and Identically Distributed (IID) assumption which make data-driven inference untrustworthy.
Presented is an automated check for such IID violations that you can quickly run on any {numeric, image, text, audio, etc.} dataset! This method helps you understand: does the order in which my data were collected matter? When the answer is yes, you must take special precautions in modeling to ensure proper generalization from data violating the IID property. Almost all of standard Machine Learning and Statistics relies on this fundamental property!
Don’t let such issues mess up your data analysis, use automated software to detect them before you dive into modeling!
Distribution drift and interactions between datapoints (autocorrelation) are common violations of the Independent and Identically Distributed (IID) assumption which make data-driven inference untrustworthy.
Presented is an automated check for such IID violations that you can quickly run on any {numeric, image, text, audio, etc.} dataset! This method helps you understand: does the order in which my data were collected matter? When the answer is yes, you must take special precautions in modeling to ensure proper generalization from data violating the IID property. Almost all of standard Machine Learning and Statistics relies on this fundamental property!
Don’t let such issues mess up your data analysis, use automated software to detect them before you dive into modeling!