Remove 2002 Remove Data Collection Remove Testing
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. In their 2002 paper Chawla et al. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling.

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IT leaders weigh up AI’s role to improve data management

CIO Business Intelligence

The first step of the manager’s team was instead to hire a UX designer to not only design the interface and experience for the end user, but also carry out tests to bring qualitative and quantitative evidence on site and app performance to direct the business. “E-commerce The data is then re-transported when the line is available.

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Unintentional data

The Unofficial Google Data Science Blog

1]" Statistics, as a discipline, was largely developed in a small data world. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. We must correct for multiple hypothesis tests. We ought not dredge our data.