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

The Unofficial Google Data Science Blog

The first is that they are straightforward to optimize using traditional gradient-based optimizers as long as we pre-specify the placement of the knots. There is a robust set of tools for working with these kinds of constrained optimization problems. PLFs have two useful properties that we take advantage of.

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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. deep learning) there is no guaranteed explainability. Learning from Imbalanced Data. Model training.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. 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.

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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.