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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Here’s where I get baffled by people who use words such as agile or lean to describe process for data science.

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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. Their tests are performed using C4.5-generated 1988), E-state data (Hall et al., The unreasonable effectiveness of data.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

For this demo we’ll use the freely available Statlog (German Credit Data) Data Set, which can be downloaded from Kaggle. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. After forming the X and y variables, we split the data into training and test sets.

Modeling 139
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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity! Misleading statistics refers to the misuse of numerical data either intentionally or by error. Exclusive Bonus Content: Download Our Free Data Integrity Checklist.