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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. References. link] Fisher, R.

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

Domino Data Lab

Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. Courville, Pascal Vincent, Visualizing Higher-Layer Features of a Deep Network, 2009. See Wei et al. References. Ribeiro, M.

Modeling 139