Remove Data Collection Remove Knowledge Discovery Remove Metrics Remove Risk
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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. For this demo we’ll use the freely available Statlog (German Credit Data) Data Set, which can be downloaded from Kaggle. 1 570 0 570 Name: credit, dtype: int64.

Modeling 139
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. Chawla et al.

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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

They have different metrics for judging whether some content is interesting or not. This is a knowledge that anyone can get, but it would take much longer than optimal. But still, is there a risk that AI could replace people at their workplace? Milena Yankova : That’s a very interesting question. It’s very likely.