Datasets:
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README.md
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AFRLA - Instance Level Results is a collection of predictions at the instance level for eleven different regression tasks tested on 255 different tree-based models. The aim of this dataset is to provide example-level results to train assessor models to predict performance of the tree-based models.
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The dataset presents eleven sections (one per regression task), with varying degrees of performance, difficulty and characteristics from the original tasks. Every one of the 255 models was trained on a subset of the dataset used for every task, and the result shown here are the test (never-before-seen by the models) predictions. Each subset has:
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- An **instance identifier** indicating the instance nº from the test set. This is just an identifier and it is not usually employed for training assessors, although in some occasions it may be useful.
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- Profiling metrics such as training time, inference time or memory usage
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These metrics are not recorded per example, but rather per model (that is, if the inference time is 1.2 ms, the model predicted *the entirety* of the test dataset in that time, instead of just that example), and are then casted for each example. As such, they fully describre a model.
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AFRLA - Instance Level Results is a collection of predictions at the instance level for eleven different regression tasks tested on 255 different tree-based models. The aim of this dataset is to provide example-level results to train assessor models to predict performance of the tree-based models.
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## The dataset
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The dataset presents eleven sections (one per regression task), with varying degrees of performance, difficulty and characteristics from the original tasks. Every one of the 255 models was trained on a subset of the dataset used for every task, and the result shown here are the test (never-before-seen by the models) predictions. Each subset has:
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- An **instance identifier** indicating the instance nº from the test set. This is just an identifier and it is not usually employed for training assessors, although in some occasions it may be useful.
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- Profiling metrics such as training time, inference time or memory usage
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These metrics are not recorded per example, but rather per model (that is, if the inference time is 1.2 ms, the model predicted *the entirety* of the test dataset in that time, instead of just that example), and are then casted for each example. As such, they fully describre a model.
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## Partitions and versions
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The sections are already partitioned into a predefined train-validation-test split for training assessors. Assessors need a particular kind of partitioning (mainly stratified by instance identifier to avoid contamination), so that's why the subset are given.
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The **main** branch contains the unaltered datasets, keeping the original values of the task and model characteristics, whereas the **normalised** branch contains the datasets properly normalised.
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## Original tasks
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| **Dataset** | **#Feat.** | **#Inst.** | **Cat.** | **Num.** | **Domain** |
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|--------------------------------------|------------|------------|----------|----------|------------|
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| Abalone | 8 | 4177 | Yes | Yes | Biology |
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| Auction Verification | 8 | 2043 | Yes | Yes | Commerce |
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| BGN EchoMonts | 10 | 17496 | Yes | Yes | Health |
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| California Housing | 8 | 20640 | Yes | Yes | Real State |
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| Infrared Thermography Temperature | 33 | 1020 | Yes | Yes | Health |
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| Life Expectancy | 21 | 2938 | Yes | Yes | Health |
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| Music Popularity | 14 | 43597 | Yes | Yes | Music |
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| Parkinsons Telemonitoring (*motor*) | 20 | 5875 | No | Yes | Health |
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| Parkinsons Telemonitoring (*total*) | 20 | 5875 | No | Yes | Health |
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| Software Cost Estimation | 6 | 145 | Yes | Yes | Projects |
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