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A baseline is a method that uses heuristics, simple summary statistics, randomness, or machine learning to create predictions for a dataset.
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Which is the best baseline model for machine learning?
Common baseline models include linear regression when predicting continuous values, logistic regression when classifying structured data, pretrained convolutional neural networks for vision related tasks, and recurrent neural networks and gradient boosted trees for sequence modeling (Ameisen 2018).
Is it possible to run a baseline model?
Rendle et al. (2019) show that measuring the performance of a baseline model for a particular task is not as straightforward as it might first seem. In their work, they refer to two extensively studied datasets, Movielens and Netflix, to indicate that running baselines properly is difficult.
What are the advantages of using a baseline?
Another advantage of a baseline is that it’s easy to deploy, it is faster to train as few parameters can quickly fit to your data, and it is often simple enough to allow easy problem detection. Whenever an error pops up, it is likely due to a bug, some defect of the dataset, or due to wrong assumptions.
Last Update: Oct 2021
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Onan
22.10.2021 00:16
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Yenny
19.10.2021 03:00
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26.10.2021 07:40
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27.10.2021 06:48
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