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Found 6 papers, 0 papers with code
Mean Absolute Percentage Error for regression models
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models.
Study of a bias in the offline evaluation of a recommendation algorithm
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles.
Empirical risk minimization is consistent with the mean absolute percentage error
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models.
Using the Mean Absolute Percentage Error for Regression Models
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models.
Reducing offline evaluation bias of collaborative filtering algorithms
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles.
Reducing Offline Evaluation Bias in Recommendation Systems
Recommendation systems have been integrated into the majority of large online systems.
