---
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  - name: JitterAbs
    dtype: float64
  - name: JitterRAP
    dtype: float64
  - name: JitterPPQ5
    dtype: float64
  - name: JitterDDP
    dtype: float64
  - name: Shimmer
    dtype: float64
  - name: ShimmerdB
    dtype: float64
  - name: ShimmerAPQ3
    dtype: float64
  - name: ShimmerAPQ5
    dtype: float64
  - name: ShimmerAPQ11
    dtype: float64
  - name: ShimmerDDA
    dtype: float64
  - name: NHR
    dtype: float64
  - name: HNR
    dtype: float64
  - name: RPDE
    dtype: float64
  - name: DFA
    dtype: float64
  - name: PPE
    dtype: float64
  - name: sex
    dtype: int64
  - name: real
    dtype: float64
  - name: prediction
    dtype: float64
  - name: model
    dtype: string
  - name: cpu_training_time
    dtype: int64
  - name: cpu_prediction_time
    dtype: int64
  - name: memory_usage
    dtype: int64
  - name: max_depth
    dtype: int64
  - name: learning_rate
    dtype: float64
  - name: n_estimators
    dtype: int64
  splits:
  - name: train
    num_bytes: 76471200
    num_examples: 314925
  - name: validation
    num_bytes: 10897920
    num_examples: 44880
  - name: test
    num_bytes: 21795840
    num_examples: 89760
  download_size: 54813165
  dataset_size: 109164960
- config_name: parkinsons_total
  features:
  - name: instance
    dtype: int64
  - name: age
    dtype: int64
  - name: testTime
    dtype: float64
  - name: Jitter
    dtype: float64
  - name: JitterAbs
    dtype: float64
  - name: JitterRAP
    dtype: float64
  - name: JitterPPQ5
    dtype: float64
  - name: JitterDDP
    dtype: float64
  - name: Shimmer
    dtype: float64
  - name: ShimmerdB
    dtype: float64
  - name: ShimmerAPQ3
    dtype: float64
  - name: ShimmerAPQ5
    dtype: float64
  - name: ShimmerAPQ11
    dtype: float64
  - name: ShimmerDDA
    dtype: float64
  - name: NHR
    dtype: float64
  - name: HNR
    dtype: float64
  - name: RPDE
    dtype: float64
  - name: DFA
    dtype: float64
  - name: PPE
    dtype: float64
  - name: sex
    dtype: int64
  - name: real
    dtype: float64
  - name: prediction
    dtype: float64
  - name: model
    dtype: string
  - name: cpu_training_time
    dtype: int64
  - name: cpu_prediction_time
    dtype: int64
  - name: memory_usage
    dtype: int64
  - name: max_depth
    dtype: int64
  - name: learning_rate
    dtype: float64
  - name: n_estimators
    dtype: int64
  splits:
  - name: train
    num_bytes: 76471200
    num_examples: 314925
  - name: validation
    num_bytes: 10897920
    num_examples: 44880
  - name: test
    num_bytes: 21795840
    num_examples: 89760
  download_size: 54936377
  dataset_size: 109164960
- config_name: swCSC
  features:
  - name: instance
    dtype: int64
  - name: AFP
    dtype: float64
  - name: FEh
    dtype: int64
  - name: PT_D
    dtype: bool
  - name: PT_P
    dtype: bool
  - name: PT_Unk
    dtype: bool
  - name: FEM_A
    dtype: bool
  - name: FEM_C
    dtype: bool
  - name: FEM_CAE
    dtype: bool
  - name: FEM_D
    dtype: bool
  - name: FEM_EO
    dtype: bool
  - name: FEM_W
    dtype: bool
  - name: real
    dtype: int64
  - name: prediction
    dtype: float64
  - name: model
    dtype: string
  - name: cpu_training_time
    dtype: int64
  - name: cpu_prediction_time
    dtype: int64
  - name: memory_usage
    dtype: int64
  - name: max_depth
    dtype: int64
  - name: learning_rate
    dtype: float64
  - name: n_estimators
    dtype: int64
  splits:
  - name: train
    num_bytes: 880860
    num_examples: 8160
  - name: validation
    num_bytes: 110112
    num_examples: 1020
  - 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   |