Aggregate performance

Absolute improvement: 0.0%

Should we releases this commit? True. Why? We should release IF this has other improvements since it isn't worst

A total of 0 datasets are missing

A total of 0 datasets are performing better

A total of 0 datasets are performing worst

A total of 58 datasets are about the same


Per dataset performance


Missing


Equal


Better


Worst





Data Dump

First version (Base)

Dataset: amazon_food

Mean acc: 0.398

95% or 0.005 around the mean CI: [0.383, 0.412]


Dataset: arrivals

Mean acc: 0.128

95% or 0.005 around the mean CI: [nan, nan]


Dataset: arrivals

Mean acc: 1.44

95% or 0.005 around the mean CI: [nan, nan]


Dataset: australian_retail

Mean acc: 0.848

95% or 0.005 around the mean CI: [nan, nan]


Dataset: australian_retail

Mean acc: 0.812

95% or 0.005 around the mean CI: [nan, nan]


Dataset: automobile_insurance

Mean acc: 0.659

95% or 0.005 around the mean CI: [0.628, 0.69]


Dataset: bike_sharing_demand

Mean acc: 0.43

95% or 0.005 around the mean CI: [nan, nan]


Dataset: bike_sharing_demand

Mean acc: 0.496

95% or 0.005 around the mean CI: [nan, nan]


Dataset: chicago_transit_ts

Mean acc: 0.498

95% or 0.005 around the mean CI: [nan, nan]


Dataset: chicago_transit_ts

Mean acc: 2.079

95% or 0.005 around the mean CI: [nan, nan]


Dataset: concrete_strength

Mean acc: 0.899

95% or 0.005 around the mean CI: [0.839, 0.958]


Dataset: covid_icu_utilization

Mean acc: 0.903

95% or 0.005 around the mean CI: [nan, nan]


Dataset: covid_icu_utilization

Mean acc: 0.413

95% or 0.005 around the mean CI: [nan, nan]


Dataset: data_professional_salary

Mean acc: 0.232

95% or 0.005 around the mean CI: [0.072, 0.391]


Dataset: diamonds

Mean acc: 0.995

95% or 0.005 around the mean CI: [0.977, 1]


Dataset: dielectron

Mean acc: 0.996

95% or 0.005 around the mean CI: [0.991, 1]


Dataset: eeg_eyes

Mean acc: 0.991

95% or 0.005 around the mean CI: [nan, nan]


Dataset: hdi

Mean acc: 0.925

95% or 0.005 around the mean CI: [0.791, 1]


Dataset: heart_disease

Mean acc: 0.822

95% or 0.005 around the mean CI: [0.697, 0.948]


Dataset: home_rentals

Mean acc: 1.0

95% or 0.005 around the mean CI: [0.995, 1]


Dataset: hospital_costs

Mean acc: 0.518

95% or 0.005 around the mean CI: [0, 1]


Dataset: insurance_claims

Mean acc: 0.977

95% or 0.005 around the mean CI: [0.927, 1]


Dataset: ionosphere

Mean acc: 0.888

95% or 0.005 around the mean CI: [0.815, 0.961]


Dataset: kin8nm

Mean acc: 0.811

95% or 0.005 around the mean CI: [0.792, 0.83]


Dataset: loan_predication

Mean acc: 0.686

95% or 0.005 around the mean CI: [0.58, 0.792]


Dataset: melbourne_temps

Mean acc: 0.479

95% or 0.005 around the mean CI: [nan, nan]


Dataset: melbourne_temps

Mean acc: 0.827

95% or 0.005 around the mean CI: [nan, nan]


Dataset: micro_mass

Mean acc: 0.195

95% or 0.005 around the mean CI: [0.179, 0.211]


Dataset: mindsdb_traffic

Mean acc: 0.712

95% or 0.005 around the mean CI: [0.39, 1]


Dataset: monthly_sunspots

Mean acc: 0.737

95% or 0.005 around the mean CI: [nan, nan]


Dataset: monthly_sunspots

Mean acc: 1.061

95% or 0.005 around the mean CI: [nan, nan]


Dataset: occupancy_detection_ts

Mean acc: 0.993

95% or 0.005 around the mean CI: [nan, nan]


Dataset: openml_abalone

Mean acc: 0.124

95% or 0.005 around the mean CI: [0.082, 0.167]


Dataset: openml_amazon

Mean acc: 0.53

95% or 0.005 around the mean CI: [0.476, 0.584]


Dataset: openml_australian

Mean acc: 0.869

95% or 0.005 around the mean CI: [0.797, 0.941]


Dataset: openml_bank

Mean acc: 0.848

95% or 0.005 around the mean CI: [0.84, 0.857]


Dataset: openml_car

Mean acc: 0.911

95% or 0.005 around the mean CI: [0.845, 0.977]


Dataset: openml_creditg

Mean acc: 0.692

95% or 0.005 around the mean CI: [0.522, 0.863]


Dataset: openml_jasmine

Mean acc: 0.798

95% or 0.005 around the mean CI: [0.793, 0.803]


Dataset: openml_kc1

Mean acc: 0.572

95% or 0.005 around the mean CI: [0.483, 0.661]


Dataset: openml_mfeat

Mean acc: 0.429

95% or 0.005 around the mean CI: [0.272, 0.586]


Dataset: openml_phoneme

Mean acc: 0.847

95% or 0.005 around the mean CI: [0.787, 0.906]


Dataset: openml_segment

Mean acc: 0.974

95% or 0.005 around the mean CI: [0.954, 0.994]


Dataset: openml_sylvine

Mean acc: 0.944

95% or 0.005 around the mean CI: [0.926, 0.962]


Dataset: openml_transfusion

Mean acc: 0.65

95% or 0.005 around the mean CI: [0.494, 0.806]


Dataset: openml_vehicle

Mean acc: 0.758

95% or 0.005 around the mean CI: [0.689, 0.826]


Dataset: pageviews

Mean acc: 0.202

95% or 0.005 around the mean CI: [nan, nan]


Dataset: pageviews

Mean acc: 0.644

95% or 0.005 around the mean CI: [nan, nan]


Dataset: plantar_fasciitis

Mean acc: 0.954

95% or 0.005 around the mean CI: [0.851, 1]


Dataset: price_of_weed

Mean acc: 1.0

95% or 0.005 around the mean CI: [0.995, 1]


Dataset: stack_overflow_survey

Mean acc: 0.734

95% or 0.005 around the mean CI: [0.729, 0.739]


Dataset: travel_insurance

Mean acc: 0.751

95% or 0.005 around the mean CI: [0.746, 0.756]


Dataset: tripadvisor_binary

Mean acc: 0.879

95% or 0.005 around the mean CI: [0.865, 0.892]


Dataset: used_car_price

Mean acc: 0.951

95% or 0.005 around the mean CI: [0.946, 0.956]


Dataset: us_consumption

Mean acc: 0.123

95% or 0.005 around the mean CI: [nan, nan]


Dataset: us_consumption

Mean acc: 0.921

95% or 0.005 around the mean CI: [nan, nan]


Dataset: us_health_insurance

Mean acc: 0.854

95% or 0.005 around the mean CI: [0.837, 0.87]


Dataset: wine_quality

Mean acc: 0.29

95% or 0.005 around the mean CI: [0.242, 0.337]



Second version (Candidate)

Dataset: amazon_food

Mean acc: 0.398

95% or 0.005 around the mean CI: [0.383, 0.412]


Dataset: arrivals

Mean acc: 0.128

95% or 0.005 around the mean CI: [nan, nan]


Dataset: arrivals

Mean acc: 1.44

95% or 0.005 around the mean CI: [nan, nan]


Dataset: australian_retail

Mean acc: 0.848

95% or 0.005 around the mean CI: [nan, nan]


Dataset: australian_retail

Mean acc: 0.812

95% or 0.005 around the mean CI: [nan, nan]


Dataset: automobile_insurance

Mean acc: 0.659

95% or 0.005 around the mean CI: [0.628, 0.69]


Dataset: bike_sharing_demand

Mean acc: 0.43

95% or 0.005 around the mean CI: [nan, nan]


Dataset: bike_sharing_demand

Mean acc: 0.496

95% or 0.005 around the mean CI: [nan, nan]


Dataset: chicago_transit_ts

Mean acc: 0.498

95% or 0.005 around the mean CI: [nan, nan]


Dataset: chicago_transit_ts

Mean acc: 2.079

95% or 0.005 around the mean CI: [nan, nan]


Dataset: concrete_strength

Mean acc: 0.899

95% or 0.005 around the mean CI: [0.839, 0.958]


Dataset: covid_icu_utilization

Mean acc: 0.903

95% or 0.005 around the mean CI: [nan, nan]


Dataset: covid_icu_utilization

Mean acc: 0.413

95% or 0.005 around the mean CI: [nan, nan]


Dataset: data_professional_salary

Mean acc: 0.232

95% or 0.005 around the mean CI: [0.072, 0.391]


Dataset: diamonds

Mean acc: 0.995

95% or 0.005 around the mean CI: [0.977, 1]


Dataset: dielectron

Mean acc: 0.996

95% or 0.005 around the mean CI: [0.991, 1]


Dataset: eeg_eyes

Mean acc: 0.991

95% or 0.005 around the mean CI: [nan, nan]


Dataset: hdi

Mean acc: 0.925

95% or 0.005 around the mean CI: [0.791, 1]


Dataset: heart_disease

Mean acc: 0.822

95% or 0.005 around the mean CI: [0.697, 0.948]


Dataset: home_rentals

Mean acc: 1.0

95% or 0.005 around the mean CI: [0.995, 1]


Dataset: hospital_costs

Mean acc: 0.518

95% or 0.005 around the mean CI: [0, 1]


Dataset: insurance_claims

Mean acc: 0.977

95% or 0.005 around the mean CI: [0.927, 1]


Dataset: ionosphere

Mean acc: 0.888

95% or 0.005 around the mean CI: [0.815, 0.961]


Dataset: kin8nm

Mean acc: 0.811

95% or 0.005 around the mean CI: [0.792, 0.83]


Dataset: loan_predication

Mean acc: 0.686

95% or 0.005 around the mean CI: [0.58, 0.792]


Dataset: melbourne_temps

Mean acc: 0.479

95% or 0.005 around the mean CI: [nan, nan]


Dataset: melbourne_temps

Mean acc: 0.827

95% or 0.005 around the mean CI: [nan, nan]


Dataset: micro_mass

Mean acc: 0.195

95% or 0.005 around the mean CI: [0.179, 0.211]


Dataset: mindsdb_traffic

Mean acc: 0.712

95% or 0.005 around the mean CI: [0.39, 1]


Dataset: monthly_sunspots

Mean acc: 0.737

95% or 0.005 around the mean CI: [nan, nan]


Dataset: monthly_sunspots

Mean acc: 1.061

95% or 0.005 around the mean CI: [nan, nan]


Dataset: occupancy_detection_ts

Mean acc: 0.993

95% or 0.005 around the mean CI: [nan, nan]


Dataset: openml_abalone

Mean acc: 0.124

95% or 0.005 around the mean CI: [0.082, 0.167]


Dataset: openml_amazon

Mean acc: 0.53

95% or 0.005 around the mean CI: [0.476, 0.584]


Dataset: openml_australian

Mean acc: 0.869

95% or 0.005 around the mean CI: [0.797, 0.941]


Dataset: openml_bank

Mean acc: 0.848

95% or 0.005 around the mean CI: [0.84, 0.857]


Dataset: openml_car

Mean acc: 0.911

95% or 0.005 around the mean CI: [0.845, 0.977]


Dataset: openml_creditg

Mean acc: 0.692

95% or 0.005 around the mean CI: [0.522, 0.863]


Dataset: openml_jasmine

Mean acc: 0.798

95% or 0.005 around the mean CI: [0.793, 0.803]


Dataset: openml_kc1

Mean acc: 0.572

95% or 0.005 around the mean CI: [0.483, 0.661]


Dataset: openml_mfeat

Mean acc: 0.429

95% or 0.005 around the mean CI: [0.272, 0.586]


Dataset: openml_phoneme

Mean acc: 0.847

95% or 0.005 around the mean CI: [0.787, 0.906]


Dataset: openml_segment

Mean acc: 0.974

95% or 0.005 around the mean CI: [0.954, 0.994]


Dataset: openml_sylvine

Mean acc: 0.944

95% or 0.005 around the mean CI: [0.926, 0.962]


Dataset: openml_transfusion

Mean acc: 0.65

95% or 0.005 around the mean CI: [0.494, 0.806]


Dataset: openml_vehicle

Mean acc: 0.758

95% or 0.005 around the mean CI: [0.689, 0.826]


Dataset: pageviews

Mean acc: 0.202

95% or 0.005 around the mean CI: [nan, nan]


Dataset: pageviews

Mean acc: 0.644

95% or 0.005 around the mean CI: [nan, nan]


Dataset: plantar_fasciitis

Mean acc: 0.954

95% or 0.005 around the mean CI: [0.851, 1]


Dataset: price_of_weed

Mean acc: 1.0

95% or 0.005 around the mean CI: [0.995, 1]


Dataset: stack_overflow_survey

Mean acc: 0.734

95% or 0.005 around the mean CI: [0.729, 0.739]


Dataset: travel_insurance

Mean acc: 0.751

95% or 0.005 around the mean CI: [0.746, 0.756]


Dataset: tripadvisor_binary

Mean acc: 0.879

95% or 0.005 around the mean CI: [0.865, 0.892]


Dataset: used_car_price

Mean acc: 0.951

95% or 0.005 around the mean CI: [0.946, 0.956]


Dataset: us_consumption

Mean acc: 0.123

95% or 0.005 around the mean CI: [nan, nan]


Dataset: us_consumption

Mean acc: 0.921

95% or 0.005 around the mean CI: [nan, nan]


Dataset: us_health_insurance

Mean acc: 0.854

95% or 0.005 around the mean CI: [0.837, 0.87]


Dataset: wine_quality

Mean acc: 0.29

95% or 0.005 around the mean CI: [0.242, 0.337]