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AFRLA-instance-level-results / README.md
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metadata
dataset_info:
  - config_name: abalone
    features:
      - name: instance
        dtype: int64
      - name: Length
        dtype: float64
      - name: Diameter
        dtype: float64
      - name: Height
        dtype: float64
      - name: Whole_weight
        dtype: float64
      - name: Shucked_weight
        dtype: float64
      - name: Viscera_weight
        dtype: float64
      - name: Shell_weight
        dtype: float64
      - name: Sex_F
        dtype: int64
      - name: Sex_I
        dtype: int64
      - name: Sex_M
        dtype: int64
      - name: real
        dtype: int64
      - name: prediction
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      - name: model
        dtype: string
      - name: cpu_training_time
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      - name: cpu_prediction_time
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      - name: memory_usage
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      - name: max_depth
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      - name: learning_rate
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      - name: n_estimators
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        num_examples: 224145
      - name: validation
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      - name: test
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    download_size: 11789983
    dataset_size: 54624240
  - config_name: auction_verification
    features:
      - name: instance
        dtype: int64
      - name: process_b1_capacity
        dtype: int64
      - name: process_b2_capacity
        dtype: int64
      - name: process_b3_capacity
        dtype: int64
      - name: process_b4_capacity
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      - name: property_price
        dtype: int64
      - name: property_product
        dtype: int64
      - name: property_winner
        dtype: int64
      - name: real
        dtype: float64
      - name: prediction
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    download_size: 3180269
    dataset_size: 22950720
  - config_name: bng_echoMonths
    features:
      - name: instance
        dtype: int64
      - name: still_alive
        dtype: float64
      - name: age
        dtype: float64
      - name: pericardial
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      - name: fractional
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      - name: epss
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      - name: lvdd
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      - name: prediction
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      - name: cpu_prediction_time
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      - name: memory_usage
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      - name: max_depth
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      - name: validation
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        num_examples: 133620
      - name: test
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        num_examples: 267495
    download_size: 90967769
    dataset_size: 217938480
  - config_name: california_housing
    features:
      - name: instance
        dtype: int64
      - name: MedInc
        dtype: float64
      - name: HouseAge
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      - name: AveRooms
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        num_examples: 157845
      - name: test
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    download_size: 107814021
    dataset_size: 244460160
  - config_name: infrared
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      - name: AgeBracket2
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      - name: EthnicityHs_Lat
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        num_examples: 7650
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        num_examples: 15300
    download_size: 4365509
    dataset_size: 34832880
  - config_name: life_expectancy
    features:
      - name: instance
        dtype: int64
      - name: Year
        dtype: int64
      - name: Adult_Mortality
        dtype: float64
      - name: infant_deaths
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      - name: Alcohol
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      - name: percentage_expenditure
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      - name: Hepatitis_B
        dtype: float64
      - name: Measles
        dtype: int64
      - name: BMI
        dtype: float64
      - name: under_five_deaths
        dtype: int64
      - name: Polio
        dtype: float64
      - name: Total_expenditure
        dtype: float64
      - name: Diphtheria
        dtype: float64
      - name: HIV_AIDS
        dtype: float64
      - name: GDP
        dtype: float64
      - name: Population
        dtype: float64
      - name: thinness_AgeBracket2
        dtype: float64
      - name: thinness_AgeBracket1
        dtype: float64
      - name: Income_composition_of_resources
        dtype: float64
      - name: Schooling
        dtype: float64
      - name: Country_Afghanistan
        dtype: int64
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  - config_name: ltfsid
    features:
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      - name: Area
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      - name: Sensing Range
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      - name: Transmission Range
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  - config_name: music_popularity
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      - name: tempo
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      - name: valence
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        num_examples: 2334780
      - name: validation
        num_bytes: 67649760
        num_examples: 333540
      - name: test
        num_bytes: 135299520
        num_examples: 667080
    download_size: 261111067
    dataset_size: 676497600
  - config_name: parkinsons_motor
    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: 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/example level for eleven different regression tasks tested on 255 tree-based models (also called "base systems"). 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 for other analysis.

  • 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.

Boo

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 (numerical features are centered and scaled and categorical features are transformed into dummies).

Original tasks

Dataset #Feat. #Inst. Cat. Num. Domain
Abalone 8 4177 Yes Yes Biology
Auction Verification 8 2043 Yes Yes Commerce
BNG EchoMonts 10 17496 Yes Yes Health
California Housing 8 20640 Yes Yes Real State
Infrared Thermography Temperature 33 1020 Yes Yes Health
Intrusion detection 4 182 No Yes Computer Science
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