Remove 2013 Remove Predictive Modeling Remove Statistics Remove Testing
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Why you should care about debugging machine learning models

O'Reilly on Data

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Interpretable ML models and explainable ML.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. Let’s also look at the basic descriptive statistics for all attributes. from sklearn import metrics.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Although it’s not perfect, [Note: These are statistical approximations, of course!] GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper.

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Data Science at The New York Times

Domino Data Lab

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

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What Is Embedded Analytics?

Jet Global

Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. Advanced Analytics Some apps provide a unique value proposition through the development of advanced (and often proprietary) statistical models. Their dashboards were visually stunning.