coverforest: Conformal Predictions with Random Forest in Python
NeutralArtificial Intelligence
coverforest: Conformal Predictions with Random Forest in Python
The article discusses the advancements in conformal prediction using random forest techniques in Python, highlighting the benefits of improved data efficiency through methods like CV+ and Jackknife+-after-bootstrap. These methods offer better uncertainty quantification but come with increased computational costs. Understanding these developments is crucial for researchers and practitioners looking to enhance their predictive models while managing computational resources effectively.
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