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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. Once all packages have been imported, we can move on to loading our test data. We can now pass the preprocessed data to the Pumas NCAReport function, which calculates a wide range of relevant NCA metrics.

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. return synthetic. Chawla et al.,

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

Domino Data Lab

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. After forming the X and y variables, we split the data into training and test sets. For sample 23 from the test set, the model is leaning towards a bad credit prediction.

Modeling 139
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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

The LSOS may do this by exposing a random group of users to the new design and compare them to a control group, and then analyze the effect on important user engagement metrics, such as bounce rate, time to first action, or number of experiences deemed positive. In addition to a suitable metric, we must also choose our experimental unit.

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AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

Milena Yankova : Our work is focused on helping companies make sense of their own knowledge. Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. Some of this knowledge is locked and the company cannot access it.