Remove 2009 Remove Deep Learning Remove Optimization Remove Testing
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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

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

Domino Data Lab

deep learning) there is no guaranteed explainability. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Fraudulent transactions are 50.00% of the test set.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. Agile was originally about iterating fast on a code base and its unit tests, then getting results in front of stakeholders. Generally, you cannot get both.

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

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139
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Data Science at The New York Times

Domino Data Lab

When he retired in 2009 he had some time on his hands. or are you looking for me to help you decide on what is the optimal treatment in order to get the outcome you want?” ” And Mark said, “Yes, we’ve got a lot of data. Please help us make sense of it.” He put together this lovely collection of essays.