×
We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications.
Dec 13, 2021
People also ask
This paper studies how active learning of selective classifiers is affected by the focus on value and proposes a novel value-aware active learning strategy ...
A self-service, on-demand compute environment for data analysis and ML models increases productivity and performance while minimizing IT support and cost.
Dec 13, 2021 · PDF | We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics ...
Sep 30, 2022 · In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or ...
To properly evaluate a model, you hold out a sample of data that has been labeled with the target (ground truth) from the training datasource.
AI accuracy is the percentage of correct classifications that a trained machine learning model achieves.
Sep 14, 2021 · Here, we will explore and understand evaluation plots like Cumulative Gain plots and Lift plots, to assess the business value of ML model.
ML models mimic the way humans learn—by trial and error. Over time, well-trained models will make increasingly accurate predictions. ML models are widely used, ...
Mar 13, 2024 · In this paper, we introduced several evaluation metrics for common ML tasks including binary and multi-class classification, regression, image ...