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Model Risk Management And the Role of Explainable Models(With Python Code)

Analytics Vidhya

The post Model Risk Management And the Role of Explainable Models(With Python Code) appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Photo by h heyerlein on Unsplash Introduction Similar to rule-based mathematical.

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Gaussian Naive Bayes Algorithm for Credit Risk Modelling

Analytics Vidhya

Banks rapidly recognize the increased need for comprehensive credit risk […]. The post Gaussian Naive Bayes Algorithm for Credit Risk Modelling appeared first on Analytics Vidhya.

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OpenAI CEO Urges Lawmakers to Regulate AI Considering AI Risks

Analytics Vidhya

He took a moment to express his apprehension about the risks associated with increasingly powerful models. He […] The post OpenAI CEO Urges Lawmakers to Regulate AI Considering AI Risks appeared first on Analytics Vidhya.

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Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment

Analytics Vidhya

Cardiovascular disease (CVD) prevention is crucial for identifying at-risk individuals and providing timely intervention. However, traditional risk assessment models like the Framingham Risk Score (FRS) have shown limitations, particularly in accurately estimating risk for socioeconomically disadvantaged populations.

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XAI: Accuracy vs Interpretability for Credit-Related Models

Analytics Vidhya

When too much risk is restricted to very few players, it is considered as a notable failure of the risk management framework. […]. The post XAI: Accuracy vs Interpretability for Credit-Related Models appeared first on Analytics Vidhya.

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Government’s New Directive: Tech Companies Must Seek Permission Before Launching AI Models in India

Analytics Vidhya

Introduction In a significant development, the Indian government has mandated tech companies to obtain prior approval before deploying AI models in the country.

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ML Model Predicts Insomnia With Considerable Accuracy

Analytics Vidhya

Imagine a world where predicting a patient’s risk of developing insomnia or other sleep disorders becomes as simple as analyzing their demographic, lifestyle, and health data. Thanks to an innovative medical study, we can now use Machine Learning (ML) models to predict insomnia accurately.

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