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Sep 9, 2020 · The choice of a given rank metric is important because studies sometimes find differences in the ability of different rank metrics to predict rank-related traits, even in the same population. For example, Archie et al. [ 26 ] demonstrated that proportional rank, but not simple ordinal rank, predicted risk of injury in female baboons in the Amboseli ecosystem in Kenya [ 26 ].
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The choice of a given rank metric is important because...
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two dominance rank metrics —simple ordinal rank and proportional or ‘stan-dardized’ rank—to predict 20 traits in a wild baboon population in Amboseli, Kenya. We propose that simple ordinal rank best predicts traits when compe-tition is density-dependent, whereas proportional rank best predicts traits when competition is density-independent.
- Emily J Levy, Matthew N Zipple, Emily McLean, Fernando A Campos, Fernando A Campos, Mauna Dasari, Ar...
- 2020
May 2, 2020 · Here we compare the ability of two dominance rank metrics - ordinal rank and proportional or 'standardized' rank - to predict 20 distinct traits in a well-studied wild baboon population in ...
Jul 2, 2015 · 36. I'm interested in looking at several different metrics for ranking algorithms - there are a few listed on the Learning to Rank wikipedia page, including: • Mean average precision (MAP); • DCG and NDCG; • Precision@n, NDCG@n, where "@n" denotes that the metrics are evaluated only on top n documents; • Mean reciprocal rank;
May 2, 2020 · Across group-living animals, linear dominance hierarchies lead to disparities in access to resources, health outcomes, and reproductive performance. Studies of how dominance rank affects these outcomes typically employ one of several dominance rank metrics without examining the assumptions each metric makes about its underlying competitive processes. Here we compare the ability of two ...
- Emily J Levy, Matthew N Zipple, Emily McLean, Fernando A Campos, Mauna Dasari, Arielle S Fogel, Math...
- 2020
We can roughly group the recommender or ranking quality metric into three categories: 1. Predictive metrics. They reflect the “correctness” of recommendations and show how well the system finds relevant items. 2. Ranking metrics. They reflect the ranking quality: how well the system can sort the items from more relevant to less relevant. 3.
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Which rank metric is best for predicting traits?
Can different dominance rank metrics predict multiple traits in the same population?
What are the different metrics for ranking algorithms?
Which rank metric predicts traits when competition is density-independent?
What are ranking metrics?
What is a recommender or ranking quality metric?
Ranking and recommendation systems often focus on the relevance and order of items rather than just the correctness of prediction, as it is in classification or regression. In this guide, we look into the key metrics and explain them step by step. This guide is for data scientists, ML engineers, product managers, and anyone who deals with ...
