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--- |
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num_examples: 25245 |
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download_size: 14865507 |
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dataset_size: 227561400 |
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- config_name: ltfsid |
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features: |
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dtype: int64 |
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num_examples: 2805 |
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download_size: 222167 |
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dataset_size: 1722600 |
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- config_name: music_popularity |
|
features: |
|
- name: instance |
|
dtype: int64 |
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- name: acousticness |
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dtype: float64 |
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- name: danceability |
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- name: instrumentalness |
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- name: speechiness |
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- name: tempo |
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num_examples: 333540 |
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num_bytes: 135299520 |
|
num_examples: 667080 |
|
download_size: 261111067 |
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dataset_size: 676497600 |
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- config_name: parkinsons_motor |
|
features: |
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- name: instance |
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dtype: int64 |
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- name: age |
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- name: testTime |
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dtype: float64 |
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dtype: float64 |
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dtype: float64 |
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num_bytes: 21795840 |
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num_examples: 89760 |
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download_size: 54813165 |
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dataset_size: 109164960 |
|
- config_name: parkinsons_total |
|
features: |
|
- name: instance |
|
dtype: int64 |
|
- name: age |
|
dtype: int64 |
|
- name: testTime |
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dtype: float64 |
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- name: JitterAbs |
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dtype: float64 |
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num_examples: 89760 |
|
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|
dataset_size: 109164960 |
|
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|
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|
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|
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dtype: bool |
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dtype: bool |
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dtype: bool |
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- name: FEM_D |
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dtype: bool |
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- name: FEM_EO |
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dtype: bool |
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dtype: int64 |
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|
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num_examples: 1020 |
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- name: test |
|
num_bytes: 220215 |
|
num_examples: 2040 |
|
download_size: 199054 |
|
dataset_size: 1211187 |
|
configs: |
|
- config_name: abalone |
|
data_files: |
|
- split: train |
|
path: abalone/train-* |
|
- split: validation |
|
path: abalone/validation-* |
|
- split: test |
|
path: abalone/test-* |
|
- config_name: auction_verification |
|
data_files: |
|
- split: train |
|
path: auction_verification/train-* |
|
- split: validation |
|
path: auction_verification/validation-* |
|
- split: test |
|
path: auction_verification/test-* |
|
- config_name: bng_echoMonths |
|
data_files: |
|
- split: train |
|
path: bng_echoMonths/train-* |
|
- split: validation |
|
path: bng_echoMonths/validation-* |
|
- split: test |
|
path: bng_echoMonths/test-* |
|
- config_name: california_housing |
|
data_files: |
|
- split: train |
|
path: california_housing/train-* |
|
- split: validation |
|
path: california_housing/validation-* |
|
- split: test |
|
path: california_housing/test-* |
|
- config_name: infrared |
|
data_files: |
|
- split: train |
|
path: infrared/train-* |
|
- split: validation |
|
path: infrared/validation-* |
|
- split: test |
|
path: infrared/test-* |
|
- config_name: life_expectancy |
|
data_files: |
|
- split: train |
|
path: life_expectancy/train-* |
|
- split: validation |
|
path: life_expectancy/validation-* |
|
- split: test |
|
path: life_expectancy/test-* |
|
- config_name: ltfsid |
|
data_files: |
|
- split: train |
|
path: ltfsid/train-* |
|
- split: validation |
|
path: ltfsid/validation-* |
|
- split: test |
|
path: ltfsid/test-* |
|
- config_name: music_popularity |
|
data_files: |
|
- split: train |
|
path: music_popularity/train-* |
|
- split: validation |
|
path: music_popularity/validation-* |
|
- split: test |
|
path: music_popularity/test-* |
|
- config_name: parkinsons_motor |
|
data_files: |
|
- split: train |
|
path: parkinsons_motor/train-* |
|
- split: validation |
|
path: parkinsons_motor/validation-* |
|
- split: test |
|
path: parkinsons_motor/test-* |
|
- config_name: parkinsons_total |
|
data_files: |
|
- split: train |
|
path: parkinsons_total/train-* |
|
- split: validation |
|
path: parkinsons_total/validation-* |
|
- split: test |
|
path: parkinsons_total/test-* |
|
- config_name: swCSC |
|
data_files: |
|
- split: train |
|
path: swCSC/train-* |
|
- split: validation |
|
path: swCSC/validation-* |
|
- split: test |
|
path: swCSC/test-* |
|
task_categories: |
|
- tabular-regression |
|
modalities: |
|
- tabular |
|
--- |
|
|
|
# Assessors For Regression: Loss Analysis - Instance Level Results |
|
|
|
AFRLA - Instance Level Results is a collection of predictions at the instance level for eleven different regression tasks tested on 255 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. |
|
|
|
## The dataset |
|
|
|
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 results shown here are the test (never-before-seen by the models) predictions. Each subset has: |
|
|
|
- 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. |
|
- The **original task features**, the features used by the models to learn the task. Along with the instance identifier, they fully describe a test example. |
|
- The **model features**, descriptors of the 255 models. Mainly: |
|
- The model used (XGBoost, Random Forest, Decision Tree...) |
|
- Hyperparameters such as the maximum depth, number of estimators if applicable... |
|
- Profiling metrics such as training time, inference time or memory usage |
|
|
|
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. |
|
|
|
## Partitions and versions |
|
|
|
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 subsets are given. |
|
|
|
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. |
|
|
|
## Original tasks |
|
|
|
| **Dataset** | **#Feat.** | **#Inst.** | **Cat.** | **Num.** | **Domain** | |
|
|--------------------------------------|------------|------------|----------|----------|------------| |
|
| Abalone | 8 | 4177 | Yes | Yes | Biology | |
|
| Auction Verification | 8 | 2043 | Yes | Yes | Commerce | |
|
| BGN EchoMonts | 10 | 17496 | Yes | Yes | Health | |
|
| California Housing | 8 | 20640 | Yes | Yes | Real State | |
|
| Infrared Thermography Temperature | 33 | 1020 | Yes | Yes | Health | |
|
| Life Expectancy | 21 | 2938 | Yes | Yes | Health | |
|
| Music Popularity | 14 | 43597 | Yes | Yes | Music | |
|
| Parkinsons Telemonitoring (*motor*) | 20 | 5875 | No | Yes | Health | |
|
| Parkinsons Telemonitoring (*total*) | 20 | 5875 | No | Yes | Health | |
|
| Software Cost Estimation | 6 | 145 | Yes | Yes | Projects | |
|
|
|
|
