uid
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30 values
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stringclasses
9 values
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2
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dataset_features
stringlengths
41
3.57M
task_description
stringlengths
627
762
task_name
stringlengths
2
124
attribute_names
listlengths
0
100k
categorical_indicator
listlengths
0
100k
__index_level_0__
int64
0
3.8k
146,193
precision
precision_score
clean1
Derived from the Musk dataset: https://www.openml.org/d/1116
{0: [0 - molecule_name (numeric)], 1: [1 - conformation_name (numeric)], 2: [2 - f1 (numeric)], 3: [3 - f2 (numeric)], 4: [4 - f3 (numeric)], 5: [5 - f4 (numeric)], 6: [6 - f5 (numeric)], 7: [7 - f6 (numeric)], 8: [8 - f7 (numeric)], 9: [9 - f8 (numeric)], 10: [10 - f9 (numeric)], 11: [11 - f10 (numeric)], ...
{'MajorityClassSize': 269.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 207.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 169.0, 'NumberOfInstances': 476.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 168.0, 'NumberOfSymbolicFeatures': 1.0,...
clean1
[ "molecule_name", "conformation_name", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "f10", "f11", "f12", "f13", "f14", "f15", "f16", "f17", "f18", "f19", "f20", "f21", "f22", "f23", "f24", "f25", "f26", "f27", "f28", "f29", "f30", "f31", "f32...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,617
146,210
precision
precision_score
postoperative-patient-data
postoperative-patient-data-pmlb
{0: [0 - L-CORE (nominal)], 1: [1 - L-SURF (nominal)], 2: [2 - L-O2 (nominal)], 3: [3 - L-BP (nominal)], 4: [4 - SURF-STBL (nominal)], 5: [5 - CORE-STBL (nominal)], 6: [6 - BP-STBL (nominal)], 7: [7 - COMFORT (nominal)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 64.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 24.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 88.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 9.0, 'cost...
postoperative-patient-data
[ "L-CORE", "L-SURF", "L-O2", "L-BP", "SURF-STBL", "CORE-STBL", "BP-STBL", "COMFORT" ]
[ true, true, true, true, true, true, true, true ]
2,619
4,693
predictive_accuracy
accuracy_score
AP_Endometrium_Lung
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 117_at (numeric)], 3: [3 - 121_at (numeric)], 4: [4 - 1405_i_at (numeric)], 5: [5 - 1438_at (numeric)], 6: [6 - 1552257_a_at (numeric)], 7: [7 - 1552283_s_at (numeric)], 8: [8 - 1552309_a_at (numeric)], 9: [9 - 1552348_at (numeric)], 10: [10 - ...
{'MajorityClassSize': 126.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 61.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 187.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': 1...
AP_Endometrium_Lung
[ "1007_s_at", "117_at", "121_at", "1405_i_at", "1438_at", "1552257_a_at", "1552283_s_at", "1552309_a_at", "1552348_at", "1552349_a_at", "1552365_at", "1552368_at", "1552426_a_at", "1552477_a_at", "1552594_at", "1552610_a_at", "1552615_at", "1552619_a_at", "1552621_at", "1552622_...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,621
146,204
precision
precision_score
led24
led24-pmlb
{0: [0 - attribute#1 (nominal)], 1: [1 - attribute#2 (nominal)], 2: [2 - attribute#3 (nominal)], 3: [3 - attribute#4 (nominal)], 4: [4 - attribute#5 (nominal)], 5: [5 - attribute#6 (nominal)], 6: [6 - attribute#7 (nominal)], 7: [7 - irrelevant1 (nominal)], 8: [8 - irrelevant2 (nominal)], 9: [9 - irrelevant3 (n...
{'MajorityClassSize': 337.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 296.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 3200.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 25.0...
led24
[ "attribute#1", "attribute#2", "attribute#3", "attribute#4", "attribute#5", "attribute#6", "attribute#7", "irrelevant1", "irrelevant2", "irrelevant3", "irrelevant4", "irrelevant5", "irrelevant6", "irrelevant7", "irrelevant8", "irrelevant9", "irrelevant10", "irrelevant11", "irrelev...
[ true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true ]
2,622
146,216
precision
precision_score
threeOf9
threeOf9-pmlb
{0: [0 - F1 (nominal)], 1: [1 - F2 (nominal)], 2: [2 - F3 (nominal)], 3: [3 - F4 (nominal)], 4: [4 - F5 (nominal)], 5: [5 - F6 (nominal)], 6: [6 - F7 (nominal)], 7: [7 - F8 (nominal)], 8: [8 - F9 (nominal)], 9: [9 - class (nominal)]}
{'MajorityClassSize': 274.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 238.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10.0, 'NumberOfInstances': 512.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 10.0, ...
threeOf9
[ "F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9" ]
[ true, true, true, true, true, true, true, true, true ]
2,623
145,979
precision
precision_score
spambase
**Author**: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/spambase) **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) SPAM E-mail Database The "spam" concept is diverse: advertisements for products/websites, make mo...
{0: [0 - word_freq_make (numeric)], 1: [1 - word_freq_address (numeric)], 2: [2 - word_freq_all (numeric)], 3: [3 - word_freq_3d (numeric)], 4: [4 - word_freq_our (numeric)], 5: [5 - word_freq_over (numeric)], 6: [6 - word_freq_remove (numeric)], 7: [7 - word_freq_internet (numeric)], 8: [8 - word_freq_order (n...
{'MajorityClassSize': 2788.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 1813.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 58.0, 'NumberOfInstances': 4601.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 57.0, 'NumberOfSymbolicFeatures': 1.0...
spambase
[ "word_freq_make", "word_freq_address", "word_freq_all", "word_freq_3d", "word_freq_our", "word_freq_over", "word_freq_remove", "word_freq_internet", "word_freq_order", "word_freq_mail", "word_freq_receive", "word_freq_will", "word_freq_people", "word_freq_report", "word_freq_addresses", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,624
75,240
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small
**Author**: **Source**: Unknown - Date unknown **Please cite**: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte...
{0: [0 - year (nominal)], 1: [1 - gq (nominal)], 2: [2 - gqtypeg (nominal)], 3: [3 - farm (nominal)], 4: [4 - ownershg (nominal)], 5: [5 - value (numeric)], 6: [6 - rent (numeric)], 7: [7 - ftotinc (numeric)], 8: [8 - nfams (numeric)], 9: [9 - ncouples (numeric)], 10: [10 - nmothers (numeric)], 11: [11 - nfa...
{'MajorityClassSize': 4425.0, 'MaxNominalAttDistinctValues': 191.0, 'MinorityClassSize': 2594.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 61.0, 'NumberOfInstances': 7019.0, 'NumberOfInstancesWithMissingValues': 7019.0, 'NumberOfMissingValues': 43814.0, 'NumberOfNumericFeatures': 33.0, 'NumberOfSymbolicFeatu...
kdd_ipums_la_97-small
[ "year", "gq", "gqtypeg", "farm", "ownershg", "value", "rent", "ftotinc", "nfams", "ncouples", "nmothers", "nfathers", "momloc", "stepmom", "momrule", "poploc", "steppop", "poprule", "sploc", "sprule", "famsize", "nchild", "nchlt5", "famunit", "eldch", "yngch", "ns...
[ true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, true, true, true, true, true, true, true, true,...
2,625
146,207
precision
precision_score
mofn-3-7-10
**Author**: Unknown **Source**: [PMLB](https://github.com/EpistasisLab/penn-ml-benchmarks/tree/master/datasets/classification) Supposedly from UCI originally, but can't find it there. **Please cite** The origin is not clear, but presumably this is an artificial problem representing M-of-N rules. The target is 1 ...
{0: [0 - Bit-0 (nominal)], 1: [1 - Bit-1 (nominal)], 2: [2 - Bit-2 (nominal)], 3: [3 - Bit-3 (nominal)], 4: [4 - Bit-4 (nominal)], 5: [5 - Bit-5 (nominal)], 6: [6 - Bit-6 (nominal)], 7: [7 - Bit-7 (nominal)], 8: [8 - Bit-8 (nominal)], 9: [9 - Bit-9 (nominal)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 1032.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 292.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 1324.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 11.0,...
mofn-3-7-10
[ "Bit-0", "Bit-1", "Bit-2", "Bit-3", "Bit-4", "Bit-5", "Bit-6", "Bit-7", "Bit-8", "Bit-9" ]
[ true, true, true, true, true, true, true, true, true, true ]
2,627
4,668
predictive_accuracy
accuracy_score
AP_Uterus_Kidney
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1438_at (numeric)], 5: [5 - 1487_at (numeric)], 6: [6 - 1552256_a_at (numeric)], 7: [7 - 1552274_at (numeric)], 8: [8 - 1552275_s_at (numeric)], 9: [9 - 1552309_a_at (numeric)], 10: [10 -...
{'MajorityClassSize': 260.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 124.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 384.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': ...
AP_Uterus_Kidney
[ "1007_s_at", "121_at", "1405_i_at", "1438_at", "1487_at", "1552256_a_at", "1552274_at", "1552275_s_at", "1552309_a_at", "1552362_a_at", "1552365_at", "1552367_a_at", "1552378_s_at", "1552426_a_at", "1552455_at", "1552477_a_at", "1552509_a_at", "1552519_at", "1552610_a_at", "155...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,628
146,037
precision
precision_score
satimage
**Author**: Ashwin Srinivasan, Department of Statistics and Data Modeling, University of Strathclyde **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)) - 1993 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) The database consists of the multi-spectra...
{0: [0 - Aattr (numeric)], 1: [1 - Battr (numeric)], 2: [2 - Cattr (numeric)], 3: [3 - Dattr (numeric)], 4: [4 - Eattr (numeric)], 5: [5 - Fattr (numeric)], 6: [6 - A1attr (numeric)], 7: [7 - B2attr (numeric)], 8: [8 - C3attr (numeric)], 9: [9 - D4attr (numeric)], 10: [10 - E5attr (numeric)], 11: [11 - F6att...
{'MajorityClassSize': 1531.0, 'MaxNominalAttDistinctValues': 6.0, 'MinorityClassSize': 625.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 37.0, 'NumberOfInstances': 6430.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 36.0, 'NumberOfSymbolicFeatures': 1.0,...
satimage
[ "Aattr", "Battr", "Cattr", "Dattr", "Eattr", "Fattr", "A1attr", "B2attr", "C3attr", "D4attr", "E5attr", "F6attr", "A7attr", "B8attr", "C9attr", "D10attr", "E11attr", "F12attr", "A13attr", "B14attr", "C15attr", "D16attr", "E17attr", "F18attr", "A19attr", "B20attr", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,630
146,078
precision
precision_score
lymphoma_9classes
**Author**: **Source**: Unknown - Date unknown **Please cite**:
{0: [0 - GENE1835X (numeric)], 1: [1 - GENE1836X (numeric)], 2: [2 - GENE1865X (numeric)], 3: [3 - GENE1380X (numeric)], 4: [4 - GENE1933X (numeric)], 5: [5 - GENE1932X (numeric)], 6: [6 - GENE1931X (numeric)], 7: [7 - GENE1930X (numeric)], 8: [8 - GENE3129X (numeric)], 9: [9 - GENE3126X (numeric)], 10: [10 -...
{'MajorityClassSize': 46.0, 'MaxNominalAttDistinctValues': 9.0, 'MinorityClassSize': 2.0, 'NumberOfClasses': 9.0, 'NumberOfFeatures': 4027.0, 'NumberOfInstances': 96.0, 'NumberOfInstancesWithMissingValues': 89.0, 'NumberOfMissingValues': 19667.0, 'NumberOfNumericFeatures': 4026.0, 'NumberOfSymbolicFeatures': 1...
lymphoma_9classes
[ "GENE1835X", "GENE1836X", "GENE1865X", "GENE1380X", "GENE1933X", "GENE1932X", "GENE1931X", "GENE1930X", "GENE3129X", "GENE3126X", "GENE0X", "GENE3115X", "GENE3116X", "GENE3117X", "GENE3118X", "GENE3073X", "GENE3072X", "GENE3067X", "GENE3068X", "GENE3069X", "GENE2584X", "GEN...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,631
146,217
precision
precision_score
wine-quality-red
wine-quality-red-pmlb
{0: [0 - fixed_acidity (numeric)], 1: [1 - volatile_acidity (numeric)], 2: [2 - citric_acid (numeric)], 3: [3 - residual_sugar (numeric)], 4: [4 - chlorides (numeric)], 5: [5 - free_sulfur_dioxide (numeric)], 6: [6 - total_sulfur_dioxide (numeric)], 7: [7 - density (numeric)], 8: [8 - pH (numeric)], 9: [9 - su...
{'MajorityClassSize': 681.0, 'MaxNominalAttDistinctValues': 6.0, 'MinorityClassSize': 10.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 12.0, 'NumberOfInstances': 1599.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 11.0, 'NumberOfSymbolicFeatures': 1.0, ...
wine-quality-red
[ "fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "free_sulfur_dioxide", "total_sulfur_dioxide", "density", "pH", "sulphates", "alcohol" ]
[ false, false, false, false, false, false, false, false, false, false, false ]
2,632
4,646
predictive_accuracy
accuracy_score
GCM
**Author**: **Source**: Unknown - Date unknown **Please cite**: Multiclass cancer diagnosis using 16063 tumor gene expression signatures. PNAS, VOL 98, no 26, pp. 15149-15154, December 18, 2001. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mes...
{0: [0 - AFFX-BioB-5_at (numeric)], 1: [1 - AFFX-BioB-M_at (numeric)], 2: [2 - AFFX-BioB-3_at (numeric)], 3: [3 - AFFX-BioC-5_at (numeric)], 4: [4 - AFFX-BioC-3_at (numeric)], 5: [5 - AFFX-BioDn-5_at (numeric)], 6: [6 - AFFX-BioDn-3_at (numeric)], 7: [7 - AFFX-CreX-5_at (numeric)], 8: [8 - AFFX-CreX-3_at (numer...
{'MajorityClassSize': 30.0, 'MaxNominalAttDistinctValues': 14.0, 'MinorityClassSize': 10.0, 'NumberOfClasses': 14.0, 'NumberOfFeatures': 16064.0, 'NumberOfInstances': 190.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16063.0, 'NumberOfSymbolicFeatures': ...
GCM
[ "AFFX-BioB-5_at", "AFFX-BioB-M_at", "AFFX-BioB-3_at", "AFFX-BioC-5_at", "AFFX-BioC-3_at", "AFFX-BioDn-5_at", "AFFX-BioDn-3_at", "AFFX-CreX-5_at", "AFFX-CreX-3_at", "AFFX-BioB-5_st", "AFFX-BioB-M_st", "AFFX-BioB-3_st", "AFFX-BioC-5_st", "AFFX-BioC-3_st", "AFFX-BioDn-5_st", "AFFX-BioDn-3...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,633
146,228
precision
precision_score
solar-flare
flare-pmlb
{0: [0 - class_code (nominal)], 1: [1 - largest_spot_code (nominal)], 2: [2 - spot_dist_code (nominal)], 3: [3 - Activity (nominal)], 4: [4 - Evolution (nominal)], 5: [5 - Previous_24_hour_code (nominal)], 6: [6 - Historically-complex (nominal)], 7: [7 - become_complex (nominal)], 8: [8 - Area (nominal)], 9: [...
{'MajorityClassSize': 884.0, 'MaxNominalAttDistinctValues': 6.0, 'MinorityClassSize': 182.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 1066.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 11.0, ...
solar-flare
[ "class_code", "largest_spot_code", "spot_dist_code", "Activity", "Evolution", "Previous_24_hour_code", "Historically-complex", "become_complex", "Area", "Area_of_the_largest_spot" ]
[ true, true, true, true, true, true, true, true, true, true ]
2,634
146,214
precision
precision_score
solar-flare
**Author**: Gary Bradshaw **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/solar+flare) **Please cite**: **Solar Flare database** Relevant Information: -- The database contains 3 potential classes, one for the number of times a certain type of solar flare occured in a 24 hour period. -...
{0: [0 - largest_spot_size (nominal)], 1: [1 - spot_distribution (nominal)], 2: [2 - Activity (nominal)], 3: [3 - Evolution (nominal)], 4: [4 - Previous_24_hour_flare_activity_code (nominal)], 5: [5 - Historically-complex (nominal)], 6: [6 - Did_region_become_historically_complex (nominal)], 7: [7 - Area (nomina...
{'MajorityClassSize': 331.0, 'MaxNominalAttDistinctValues': 8.0, 'MinorityClassSize': 43.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 1066.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 13.0, ...
solar-flare
[ "largest_spot_size", "spot_distribution", "Activity", "Evolution", "Previous_24_hour_flare_activity_code", "Historically-complex", "Did_region_become_historically_complex", "Area", "Area_of_the_largest_spot", "C-class_flares_production_by_this_region", "M-class_flares_production_by_this_region",...
[ true, true, true, true, true, true, true, true, true, true, true, true ]
2,635
145,969
precision
precision_score
optdigits
**Author**: E. Alpaydin, C. Kaynak **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/optical+recognition+of+handwritten+digits) **Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) 1. Title of Database: Optical Recognition of Handwritten Digits 2. Source: E. Alp...
{0: [0 - input1 (numeric)], 1: [1 - input2 (numeric)], 2: [2 - input3 (numeric)], 3: [3 - input4 (numeric)], 4: [4 - input5 (numeric)], 5: [5 - input6 (numeric)], 6: [6 - input7 (numeric)], 7: [7 - input8 (numeric)], 8: [8 - input9 (numeric)], 9: [9 - input10 (numeric)], 10: [10 - input11 (numeric)], 11: [11...
{'MajorityClassSize': 572.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 554.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 65.0, 'NumberOfInstances': 5620.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 64.0, 'NumberOfSymbolicFeatures': 1.0...
optdigits
[ "input1", "input2", "input3", "input4", "input5", "input6", "input7", "input8", "input9", "input10", "input11", "input12", "input13", "input14", "input15", "input16", "input17", "input18", "input19", "input20", "input21", "input22", "input23", "input24", "input25", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,636
146,235
precision
precision_score
analcatdata_happiness
analcatdata_happiness-pmlb
{0: [0 - Years_of_schooling (nominal)], 1: [1 - Siblings (nominal)], 2: [2 - Count (numeric)], 3: [3 - class (nominal)]}
{'MajorityClassSize': 20.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 20.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 4.0, 'NumberOfInstances': 60.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 1.0, 'NumberOfSymbolicFeatures': 3.0, 'cost...
analcatdata_happiness
[ "Years_of_schooling", "Siblings", "Count" ]
[ true, true, false ]
2,637
146,077
precision
precision_score
lymphoma_2classes
**Author**: **Source**: Unknown - Date unknown **Please cite**:
{0: [0 - GENE1835X (numeric)], 1: [1 - GENE1836X (numeric)], 2: [2 - GENE1865X (numeric)], 3: [3 - GENE1380X (numeric)], 4: [4 - GENE1933X (numeric)], 5: [5 - GENE1932X (numeric)], 6: [6 - GENE1931X (numeric)], 7: [7 - GENE1930X (numeric)], 8: [8 - GENE3129X (numeric)], 9: [9 - GENE3126X (numeric)], 10: [10 -...
{'MajorityClassSize': 23.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 22.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 4027.0, 'NumberOfInstances': 45.0, 'NumberOfInstancesWithMissingValues': 38.0, 'NumberOfMissingValues': 5948.0, 'NumberOfNumericFeatures': 4026.0, 'NumberOfSymbolicFeatures': 1...
lymphoma_2classes
[ "GENE1835X", "GENE1836X", "GENE1865X", "GENE1380X", "GENE1933X", "GENE1932X", "GENE1931X", "GENE1930X", "GENE3129X", "GENE3126X", "GENE0X", "GENE3115X", "GENE3116X", "GENE3117X", "GENE3118X", "GENE3073X", "GENE3072X", "GENE3067X", "GENE3068X", "GENE3069X", "GENE2584X", "GEN...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,638
146,232
precision
precision_score
parity5_plus_5
parity5_plus_5-pmlb
{0: [0 - Bit_1 (nominal)], 1: [1 - Bit_2 (nominal)], 2: [2 - Bit_3 (nominal)], 3: [3 - Bit_4 (nominal)], 4: [4 - Bit_5 (nominal)], 5: [5 - Bit_6 (nominal)], 6: [6 - Bit_7 (nominal)], 7: [7 - Bit_8 (nominal)], 8: [8 - Bit_9 (nominal)], 9: [9 - Bit_10 (nominal)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 567.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 557.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 1124.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 11.0, ...
parity5_plus_5
[ "Bit_1", "Bit_2", "Bit_3", "Bit_4", "Bit_5", "Bit_6", "Bit_7", "Bit_8", "Bit_9", "Bit_10" ]
[ true, true, true, true, true, true, true, true, true, true ]
2,639
146,236
precision
precision_score
cleve
cleve-pmlb
{0: [0 - Age (numeric)], 1: [1 - Sex (nominal)], 2: [2 - Chest_pain_type (nominal)], 3: [3 - Trestbps (numeric)], 4: [4 - Cholesterol (numeric)], 5: [5 - Fasting_blood_sugar_<_120 (nominal)], 6: [6 - Resting_ecg (nominal)], 7: [7 - Max_heart_rate (numeric)], 8: [8 - Exercise_induced_angina (nominal)], 9: [9...
{'MajorityClassSize': 165.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 138.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 14.0, 'NumberOfInstances': 303.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 9.0, '...
cleve
[ "Age", "Sex", "Chest_pain_type", "Trestbps", "Cholesterol", "Fasting_blood_sugar_<_120", "Resting_ecg", "Max_heart_rate", "Exercise_induced_angina", "Oldpeak", "Slope", "Number_of_vessels_colored", "Thal" ]
[ false, true, true, false, false, true, true, false, true, false, true, true, true ]
2,640
146,237
precision
precision_score
cleveland-nominal
cleveland-nominal-pmlb
{0: [0 - sex (nominal)], 1: [1 - cp (nominal)], 2: [2 - fbs (nominal)], 3: [3 - restecg (nominal)], 4: [4 - exang (nominal)], 5: [5 - slope (nominal)], 6: [6 - thal (nominal)], 7: [7 - class (nominal)]}
{'MajorityClassSize': 164.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 13.0, 'NumberOfClasses': 5.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 303.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 8.0, 'co...
cleveland-nominal
[ "sex", "cp", "fbs", "restecg", "exang", "slope", "thal" ]
[ true, true, true, true, true, true, true ]
2,641
146,240
precision
precision_score
parity5
parity5-pmlb
{0: [0 - Bit_1 (nominal)], 1: [1 - Bit_2 (nominal)], 2: [2 - Bit_3 (nominal)], 3: [3 - Bit_4 (nominal)], 4: [4 - Bit_5 (nominal)], 5: [5 - class (nominal)]}
{'MajorityClassSize': 16.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 16.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 32.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 6.0, 'cost...
parity5
[ "Bit_1", "Bit_2", "Bit_3", "Bit_4", "Bit_5" ]
[ true, true, true, true, true ]
2,642
146,575
predictive_accuracy
accuracy_score
irish
**Author**: Vincent Greaney, Thomas Kelleghan (St. Patrick's College, Dublin) **Source**: [StatLib](http://lib.stat.cmu.edu/datasets/irish.ed) - 1984 **Please cite**: [StatLib](http://lib.stat.cmu.edu/datasets/) **Irish Educational Transitions Data** Data on educational transitions for a sample of 500 Irish sch...
{0: [0 - Sex (nominal)], 1: [1 - DVRT (numeric)], 2: [2 - Educational_level (nominal)], 3: [3 - Leaving_Certificate (nominal)], 4: [4 - Prestige_score (numeric)], 5: [5 - Type_school (nominal)]}
{'MajorityClassSize': 278.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 222.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 500.0, 'NumberOfInstancesWithMissingValues': 32.0, 'NumberOfMissingValues': 32.0, 'NumberOfNumericFeatures': 2.0, 'NumberOfSymbolicFeatures': 4.0, ...
irish
[ "Sex", "DVRT", "Educational_level", "Prestige_score", "Type_school" ]
[ true, false, true, false, true ]
2,643
146,080
precision
precision_score
lymphoma_11classes
**Author**: **Source**: Unknown - Date unknown **Please cite**:
{0: [0 - GENE1835X (numeric)], 1: [1 - GENE1836X (numeric)], 2: [2 - GENE1865X (numeric)], 3: [3 - GENE1380X (numeric)], 4: [4 - GENE1933X (numeric)], 5: [5 - GENE1932X (numeric)], 6: [6 - GENE1931X (numeric)], 7: [7 - GENE1930X (numeric)], 8: [8 - GENE3129X (numeric)], 9: [9 - GENE3126X (numeric)], 10: [10 -...
{'MajorityClassSize': 23.0, 'MaxNominalAttDistinctValues': 11.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 11.0, 'NumberOfFeatures': 4027.0, 'NumberOfInstances': 96.0, 'NumberOfInstancesWithMissingValues': 89.0, 'NumberOfMissingValues': 19667.0, 'NumberOfNumericFeatures': 4026.0, 'NumberOfSymbolicFeatures':...
lymphoma_11classes
[ "GENE1835X", "GENE1836X", "GENE1865X", "GENE1380X", "GENE1933X", "GENE1932X", "GENE1931X", "GENE1930X", "GENE3129X", "GENE3126X", "GENE0X", "GENE3115X", "GENE3116X", "GENE3117X", "GENE3118X", "GENE3073X", "GENE3072X", "GENE3067X", "GENE3068X", "GENE3069X", "GENE2584X", "GEN...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,644
146,577
predictive_accuracy
accuracy_score
analcatdata_dmft
**Author**: Unknown **Source**: [Jeffrey S. Simonoff](http://people.stern.nyu.edu/jsimonof/AnalCatData/Data/) - 2003 **Please cite**: Jeffrey S. Simonoff, Analyzing Categorical Data, Springer-Verlag, 2003 One of the datasets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff. It contains data...
{0: [0 - DMFT.Begin (nominal)], 1: [1 - DMFT.End (nominal)], 2: [2 - Gender (nominal)], 3: [3 - Ethnic (nominal)], 4: [4 - Prevention (nominal)]}
{'MajorityClassSize': 155.0, 'MaxNominalAttDistinctValues': 9.0, 'MinorityClassSize': 123.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 797.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 5.0, 'c...
analcatdata_dmft
[ "DMFT.Begin", "DMFT.End", "Gender", "Ethnic" ]
[ true, true, true, true ]
2,645
146,227
precision
precision_score
churn
**Author**: Unknown **Source**: [PMLB](https://github.com/EpistasisLab/penn-ml-benchmarks/tree/master/datasets/classification), [BigML](https://bigml.com/user/francisco/gallery/dataset/5163ad540c0b5e5b22000383), Supposedly from UCI but I can't find it there. **Please cite**: A dataset relating characteristics o...
{0: [0 - state (numeric)], 1: [1 - account_length (numeric)], 2: [2 - area_code (nominal)], 3: [3 - phone_number (numeric)], 4: [4 - international_plan (nominal)], 5: [5 - voice_mail_plan (nominal)], 6: [6 - number_vmail_messages (numeric)], 7: [7 - total_day_minutes (numeric)], 8: [8 - total_day_calls (numeric...
{'MajorityClassSize': 4293.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 707.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 5000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16.0, 'NumberOfSymbolicFeatures': 5.0...
churn
[ "state", "account_length", "area_code", "phone_number", "international_plan", "voice_mail_plan", "number_vmail_messages", "total_day_minutes", "total_day_calls", "total_day_charge", "total_eve_minutes", "total_eve_calls", "total_eve_charge", "total_night_minutes", "total_night_calls", ...
[ false, false, true, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, true ]
2,646
146,587
predictive_accuracy
accuracy_score
blood-transfusion-service-center
**Author**: Prof. I-Cheng Yeh **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center) **Please cite**: Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence", Expert Systems with Applications, 2008. **Blood Transfusion S...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - Class (nominal)]}
{'MajorityClassSize': 570.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 178.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 748.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
blood-transfusion-service-center
[ "V1", "V2", "V3", "V4" ]
[ false, false, false, false ]
2,647
146,586
predictive_accuracy
accuracy_score
banknote-authentication
Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/banknote+authentication) - 2012 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Dataset about distinguishing genuine and forged banknotes. Data were extracted fr...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - Class (nominal)]}
{'MajorityClassSize': 762.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 610.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 1372.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, '...
banknote-authentication
[ "V1", "V2", "V3", "V4" ]
[ false, false, false, false ]
2,648
146,578
predictive_accuracy
accuracy_score
profb
**Author**: Hal Stern, Robin Lock **Source**: [StatLib](http://lib.stat.cmu.edu/datasets/profb) **Please cite**: PRO FOOTBALL SCORES (raw data appears after the description below) How well do the oddsmakers of Las Vegas predict the outcome of professional football games? Is there really a home field advanta...
{0: [0 - Home/Away (nominal)], 1: [1 - Favorite_Points (numeric)], 2: [2 - Underdog_Points (numeric)], 3: [3 - Pointspread (numeric)], 4: [4 - Favorite_Name (nominal)], 5: [5 - Underdog_name (nominal)], 6: [6 - Year (numeric)], 7: [7 - Week (numeric)], 8: [8 - Weekday (nominal)], 9: [9 - Overtime (nominal)]}
{'MajorityClassSize': 448.0, 'MaxNominalAttDistinctValues': 28.0, 'MinorityClassSize': 224.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10.0, 'NumberOfInstances': 672.0, 'NumberOfInstancesWithMissingValues': 666.0, 'NumberOfMissingValues': 1200.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 5...
profb
[ "Favorite_Points", "Underdog_Points", "Pointspread", "Favorite_Name", "Underdog_name", "Year", "Week", "Weekday", "Overtime" ]
[ false, false, false, true, true, false, false, true, true ]
2,649
146,602
predictive_accuracy
accuracy_score
dresses-sales
**Author**: Muhammad Usman & Adeel Ahmed **Source**: origin source at [UCI](https://archive.ics.uci.edu/ml/datasets/Dresses_Attribute_Sales) **Please cite**: [Paper that claims to first have used the data](https://www.researchgate.net/profile/Dalia_Jasim/publication/293464737_main_steps_for_doing_data_mining_proje...
{0: [0 - V2 (nominal)], 1: [1 - V3 (nominal)], 2: [2 - V4 (numeric)], 3: [3 - V5 (nominal)], 4: [4 - V6 (nominal)], 5: [5 - V7 (nominal)], 6: [6 - V8 (nominal)], 7: [7 - V9 (nominal)], 8: [8 - V10 (nominal)], 9: [9 - V11 (nominal)], 10: [10 - V12 (nominal)], 11: [11 - V13 (nominal)], 12: [12 - Class (nomina...
{'MajorityClassSize': 290.0, 'MaxNominalAttDistinctValues': 24.0, 'MinorityClassSize': 210.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 500.0, 'NumberOfInstancesWithMissingValues': 401.0, 'NumberOfMissingValues': 835.0, 'NumberOfNumericFeatures': 1.0, 'NumberOfSymbolicFeatures': 12...
dresses-sales
[ "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13" ]
[ true, true, false, true, true, true, true, true, true, true, true, true ]
2,650
4,703
predictive_accuracy
accuracy_score
AP_Endometrium_Uterus
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1438_at (numeric)], 5: [5 - 1552257_a_at (numeric)], 6: [6 - 1552261_at (numeric)], 7: [7 - 1552309_a_at (numeric)], 8: [8 - 1552348_at (numeric)], 9: [9 - 1552349_a_at (numeric)], 10: [1...
{'MajorityClassSize': 124.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 61.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 185.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': 1...
AP_Endometrium_Uterus
[ "1007_s_at", "121_at", "1405_i_at", "1438_at", "1552257_a_at", "1552261_at", "1552309_a_at", "1552348_at", "1552349_a_at", "1552368_at", "1552378_s_at", "1552426_a_at", "1552455_at", "1552477_a_at", "1552594_at", "1552610_a_at", "1552615_at", "1552619_a_at", "1552621_at", "1552...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,651
146,233
precision
precision_score
allbp
allbp-pmlb
{0: [0 - age (numeric)], 1: [1 - sex (nominal)], 2: [2 - on_thyroxine (nominal)], 3: [3 - query_on_thyroxine (nominal)], 4: [4 - on_antithyroid_medication (nominal)], 5: [5 - sick (nominal)], 6: [6 - pregnant (nominal)], 7: [7 - thyroid_surgery (nominal)], 8: [8 - I131_treatment (nominal)], 9: [9 - query_hypot...
{'MajorityClassSize': 3609.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 14.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 30.0, 'NumberOfInstances': 3772.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 24.0, ...
allbp
[ "age", "sex", "on_thyroxine", "query_on_thyroxine", "on_antithyroid_medication", "sick", "pregnant", "thyroid_surgery", "I131_treatment", "query_hypothyroid", "query_hyperthyroid", "lithium", "goitre", "tumor", "hypopituitary", "psych", "TSH_measured", "TSH", "T3_measured", "T3...
[ false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, true, false, true, false, true, false, true, false, true, true, true ]
2,652
146,239
precision
precision_score
dis
dis-pmlb
{0: [0 - age (numeric)], 1: [1 - sex (nominal)], 2: [2 - on_thyroxine (nominal)], 3: [3 - query_on_thyroxine (nominal)], 4: [4 - on_antithyroid_medication (nominal)], 5: [5 - sick (nominal)], 6: [6 - pregnant (nominal)], 7: [7 - thyroid_surgery (nominal)], 8: [8 - I131_treatment (nominal)], 9: [9 - query_hypot...
{'MajorityClassSize': 3714.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 58.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 30.0, 'NumberOfInstances': 3772.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 24.0, ...
dis
[ "age", "sex", "on_thyroxine", "query_on_thyroxine", "on_antithyroid_medication", "sick", "pregnant", "thyroid_surgery", "I131_treatment", "query_hypothyroid", "query_hyperthyroid", "lithium", "goitre", "tumor", "hypopituitary", "psych", "TSH_measured", "TSH", "T3_measured", "T3...
[ false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, true, false, true, false, true, false, true, false, true, true, true ]
2,653
146,593
predictive_accuracy
accuracy_score
ilpd
**Author**: Bendi Venkata Ramana, M. Surendra Prasad Babu, N. B. Venkateswarlu **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset)) - 2012 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **Indian Liver Patient Dataset** This data set contain...
{0: [0 - V1 (numeric)], 1: [1 - V2 (nominal)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - Class (nominal)]}
{'MajorityClassSize': 416.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 167.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 583.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 9.0, 'NumberOfSymbolicFeatures': 2.0, '...
ilpd
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10" ]
[ false, true, false, false, false, false, false, false, false, false ]
2,654
146,234
precision
precision_score
allrep
allrep-pmlb
{0: [0 - age (numeric)], 1: [1 - sex (nominal)], 2: [2 - on_thyroxine (nominal)], 3: [3 - query_on_thyroxine (nominal)], 4: [4 - on_antithyroid_medication (nominal)], 5: [5 - sick (nominal)], 6: [6 - pregnant (nominal)], 7: [7 - thyroid_surgery (nominal)], 8: [8 - I131_treatment (nominal)], 9: [9 - query_hypot...
{'MajorityClassSize': 3648.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 34.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 30.0, 'NumberOfInstances': 3772.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 24.0, ...
allrep
[ "age", "sex", "on_thyroxine", "query_on_thyroxine", "on_antithyroid_medication", "sick", "pregnant", "thyroid_surgery", "I131_treatment", "query_hypothyroid", "query_hyperthyroid", "lithium", "goitre", "tumor", "hypopituitary", "psych", "TSH_measured", "TSH", "T3_measured", "T3...
[ false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, true, false, true, false, true, false, true, false, true, true, true ]
2,655
146,583
predictive_accuracy
accuracy_score
kc2
**Author**: Mike Chapman, NASA **Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/kc2.html) - 2004 **Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, C...
{0: [0 - loc (numeric)], 1: [1 - v(g) (numeric)], 2: [2 - ev(g) (numeric)], 3: [3 - iv(g) (numeric)], 4: [4 - n (numeric)], 5: [5 - v (numeric)], 6: [6 - l (numeric)], 7: [7 - d (numeric)], 8: [8 - i (numeric)], 9: [9 - e (numeric)], 10: [10 - b (numeric)], 11: [11 - t (numeric)], 12: [12 - lOCode (numeric)...
{'MajorityClassSize': 415.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 107.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 22.0, 'NumberOfInstances': 522.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 21.0, 'NumberOfSymbolicFeatures': 1.0, ...
kc2
[ "loc", "v(g)", "ev(g)", "iv(g)", "n", "v", "l", "d", "i", "e", "b", "t", "lOCode", "lOComment", "lOBlank", "lOCodeAndComment", "uniq_Op", "uniq_Opnd", "total_Op", "total_Opnd", "branchCount" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,656
146,588
predictive_accuracy
accuracy_score
climate-model-simulation-crashes
**Author**: D. Lucas, R. Klein, J. Tannahill, D. Ivanova, S. Brandon, D. Domyancic, Y. Zhang. **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/climate+model+simulation+crashes) **Please Cite**: Lucas, D. D., Klein, R., Tannahill, J., Ivanova, D., Brandon, S., Domyancic, D., and Zhang, Y.: Failure analysis of...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 494.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 46.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 540.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0, '...
climate-model-simulation-crashes
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,657
146,603
predictive_accuracy
accuracy_score
LED-display-domain-7digit
**Author**: Breiman,L., Friedman,J.H., Olshen,R.A., and Stone,C.J. **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/LED+Display+Domain), [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=63, https://archive.ics.uci.edu/ml/datasets/LED+Display+Domain) - 1988 **Please cite**: [UCI](https://archive.ics.uci.edu/m...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - Class (nominal)]}
{'MajorityClassSize': 57.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 37.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 500.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
LED-display-domain-7digit
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7" ]
[ false, false, false, false, false, false, false ]
2,658
4,675
predictive_accuracy
accuracy_score
AP_Lung_Uterus
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1438_at (numeric)], 5: [5 - 1552257_a_at (numeric)], 6: [6 - 1552283_s_at (numeric)], 7: [7 - 1552309_a_at (numeric)], 8: [8 - 1552348_at (numeric)], 9: [9 - 1552365_at (numeric)], 10: [1...
{'MajorityClassSize': 126.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 124.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 250.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': ...
AP_Lung_Uterus
[ "1007_s_at", "121_at", "1405_i_at", "1438_at", "1552257_a_at", "1552283_s_at", "1552309_a_at", "1552348_at", "1552365_at", "1552368_at", "1552378_s_at", "1552426_a_at", "1552477_a_at", "1552610_a_at", "1552615_at", "1552619_a_at", "1552621_at", "1552622_s_at", "1552626_a_at", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,659
166,850
predictive_accuracy
accuracy_score
iris
**Author**: R.A. Fisher **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall **Please cite**: **Iris Plants Database** This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is ref...
{0: [0 - sepallength (numeric)], 1: [1 - sepalwidth (numeric)], 2: [2 - petallength (numeric)], 3: [3 - petalwidth (numeric)], 4: [4 - class (nominal)]}
{'MajorityClassSize': 50.0, 'MaxNominalAttDistinctValues': 3.0, 'MinorityClassSize': 50.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 150.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'cos...
iris
[ "sepallength", "sepalwidth", "petallength", "petalwidth" ]
[ false, false, false, false ]
2,660
167,206
area_under_roc_curve
roc_auc_score
banknote-authentication
Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/banknote+authentication) - 2012 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Dataset about distinguishing genuine and forged banknotes. Data were extracted fr...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - Class (nominal)]}
{'MajorityClassSize': 762.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 610.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 1372.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, '...
banknote-authentication
[ "V1", "V2", "V3", "V4" ]
[ false, false, false, false ]
2,661
146,576
predictive_accuracy
accuracy_score
analcatdata_authorship
**Author**: **Source**: Unknown - Date unknown **Please cite**: analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two versions of each of 84 data sets, plus this READM...
{0: [0 - a (numeric)], 1: [1 - all (numeric)], 2: [2 - also (numeric)], 3: [3 - an (numeric)], 4: [4 - and (numeric)], 5: [5 - any (numeric)], 6: [6 - are (numeric)], 7: [7 - as (numeric)], 8: [8 - at (numeric)], 9: [9 - be (numeric)], 10: [10 - been (numeric)], 11: [11 - but (numeric)], 12: [12 - by (numer...
{'MajorityClassSize': 317.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 55.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 71.0, 'NumberOfInstances': 841.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 70.0, 'NumberOfSymbolicFeatures': 1.0, '...
analcatdata_authorship
[ "a", "all", "also", "an", "and", "any", "are", "as", "at", "be", "been", "but", "by", "can", "do", "down", "even", "every", "for", "from", "had", "has", "have", "her", "his", "if", "in", "into", "is", "it", "its", "may", "more", "must", "my", "no...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,662
168,313
area_under_roc_curve
roc_auc_score
iris
**Author**: **Source**: Unknown - Date unknown **Please cite**: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte...
{0: [0 - sepallength (numeric)], 1: [1 - sepalwidth (numeric)], 2: [2 - petallength (numeric)], 3: [3 - petalwidth (numeric)], 4: [4 - binaryClass (nominal)]}
{'MajorityClassSize': 100.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 50.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 150.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'co...
iris
[ "sepallength", "sepalwidth", "petallength", "petalwidth" ]
[ false, false, false, false ]
2,663
167,208
area_under_roc_curve
roc_auc_score
banknote-authentication
Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/banknote+authentication) - 2012 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Dataset about distinguishing genuine and forged banknotes. Data were extracted fr...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - Class (nominal)]}
{'MajorityClassSize': 762.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 610.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 1372.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, '...
banknote-authentication
[ "V1", "V2", "V3", "V4" ]
[ false, false, false, false ]
2,664
146,573
predictive_accuracy
accuracy_score
JapaneseVowels
**Author**: Mineichi Kudo, Jun Toyama, Masaru Shimbo **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels) **Please cite**: **Japanese vowels** This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers. The data was collected for examining our...
{0: [0 - speaker (nominal)], 1: [1 - utterance (numeric)], 2: [2 - frame (numeric)], 3: [3 - coefficient1 (numeric)], 4: [4 - coefficient2 (numeric)], 5: [5 - coefficient3 (numeric)], 6: [6 - coefficient4 (numeric)], 7: [7 - coefficient5 (numeric)], 8: [8 - coefficient6 (numeric)], 9: [9 - coefficient7 (numeri...
{'MajorityClassSize': 1614.0, 'MaxNominalAttDistinctValues': 9.0, 'MinorityClassSize': 782.0, 'NumberOfClasses': 9.0, 'NumberOfFeatures': 15.0, 'NumberOfInstances': 9961.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 14.0, 'NumberOfSymbolicFeatures': 1.0,...
JapaneseVowels
[ "utterance", "frame", "coefficient1", "coefficient2", "coefficient3", "coefficient4", "coefficient5", "coefficient6", "coefficient7", "coefficient8", "coefficient9", "coefficient10", "coefficient11", "coefficient12" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,665
168,314
area_under_roc_curve
roc_auc_score
iris
**Author**: R.A. Fisher **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall **Please cite**: **Iris Plants Database** This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is ref...
{0: [0 - sepallength (numeric)], 1: [1 - sepalwidth (numeric)], 2: [2 - petallength (numeric)], 3: [3 - petalwidth (numeric)], 4: [4 - class (nominal)]}
{'MajorityClassSize': 50.0, 'MaxNominalAttDistinctValues': 3.0, 'MinorityClassSize': 50.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 150.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'cos...
iris
[ "sepallength", "sepalwidth", "petallength", "petalwidth" ]
[ false, false, false, false ]
2,666
4,695
predictive_accuracy
accuracy_score
AP_Omentum_Ovary
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1552256_a_at (numeric)], 5: [5 - 1552257_a_at (numeric)], 6: [6 - 1552261_at (numeric)], 7: [7 - 1552348_at (numeric)], 8: [8 - 1552349_a_at (numeric)], 9: [9 - 1552365_at (numeric)], 10:...
{'MajorityClassSize': 198.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 77.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 275.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': 1...
AP_Omentum_Ovary
[ "1007_s_at", "121_at", "1405_i_at", "1552256_a_at", "1552257_a_at", "1552261_at", "1552348_at", "1552349_a_at", "1552365_at", "1552368_at", "1552426_a_at", "1552456_a_at", "1552477_a_at", "1552566_at", "1552594_at", "1552610_a_at", "1552619_a_at", "1552621_at", "1552622_s_at", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,667
4,698
predictive_accuracy
accuracy_score
AP_Endometrium_Ovary
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1438_at (numeric)], 5: [5 - 1552256_a_at (numeric)], 6: [6 - 1552257_a_at (numeric)], 7: [7 - 1552261_at (numeric)], 8: [8 - 1552309_a_at (numeric)], 9: [9 - 1552348_at (numeric)], 10: [1...
{'MajorityClassSize': 198.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 61.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 259.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': 1...
AP_Endometrium_Ovary
[ "1007_s_at", "121_at", "1405_i_at", "1438_at", "1552256_a_at", "1552257_a_at", "1552261_at", "1552309_a_at", "1552348_at", "1552349_a_at", "1552365_at", "1552368_at", "1552426_a_at", "1552477_a_at", "1552566_at", "1552594_at", "1552610_a_at", "1552621_at", "1552622_s_at", "1552...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,668
146,596
predictive_accuracy
accuracy_score
wdbc
**Author**: William H. Wolberg, W. Nick Street, Olvi L. Mangasarian **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)), [University of Wisconsin](http://pages.cs.wisc.edu/~olvi/uwmp/cancer.html) - 1995 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 357.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 212.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 31.0, 'NumberOfInstances': 569.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 30.0, 'NumberOfSymbolicFeatures': 1.0, ...
wdbc
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,669
146,547
predictive_accuracy
accuracy_score
mushroom
**Author**: [Jeff Schlimmer](Jeffrey.Schlimmer@a.gp.cs.cmu.edu) **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/mushroom) - 1981 **Please cite**: The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf ### Description This dataset descri...
{0: [0 - cap-shape (nominal)], 1: [1 - cap-surface (nominal)], 2: [2 - cap-color (nominal)], 3: [3 - bruises%3F (nominal)], 4: [4 - odor (nominal)], 5: [5 - gill-attachment (nominal)], 6: [6 - gill-spacing (nominal)], 7: [7 - gill-size (nominal)], 8: [8 - gill-color (nominal)], 9: [9 - stalk-shape (nominal)], ...
{'MajorityClassSize': 4208.0, 'MaxNominalAttDistinctValues': 12.0, 'MinorityClassSize': 3916.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 23.0, 'NumberOfInstances': 8124.0, 'NumberOfInstancesWithMissingValues': 2480.0, 'NumberOfMissingValues': 2480.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures...
mushroom
[ "cap-shape", "cap-surface", "cap-color", "bruises%3F", "odor", "gill-attachment", "gill-spacing", "gill-size", "gill-color", "stalk-shape", "stalk-root", "stalk-surface-above-ring", "stalk-surface-below-ring", "stalk-color-above-ring", "stalk-color-below-ring", "veil-type", "veil-col...
[ true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true ]
2,670
146,804
1
accuracy_score
credit-g
**Author**: Dr. Hans Hofmann **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **German Credit dataset** This dataset classifies people described by a set of attributes as good or bad credit...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0,...
credit-g
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
2,672
4,683
predictive_accuracy
accuracy_score
AP_Prostate_Kidney
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1487_at (numeric)], 5: [5 - 1552256_a_at (numeric)], 6: [6 - 1552257_a_at (numeric)], 7: [7 - 1552274_at (numeric)], 8: [8 - 1552275_s_at (numeric)], 9: [9 - 1552302_at (numeric)], 10: [1...
{'MajorityClassSize': 260.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 69.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 329.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': 1...
AP_Prostate_Kidney
[ "1007_s_at", "121_at", "1405_i_at", "1487_at", "1552256_a_at", "1552257_a_at", "1552274_at", "1552275_s_at", "1552302_at", "1552309_a_at", "1552316_a_at", "1552362_a_at", "1552365_at", "1552367_a_at", "1552426_a_at", "1552455_at", "1552463_at", "1552477_a_at", "1552509_a_at", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,673
168,350
predictive_accuracy
accuracy_score
phoneme
**Author**: Dominique Van Cappel, THOMSON-SINTRA **Source**: [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=105#sub2), [ELENA](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/phoneme/) - 1993 **Please cite**: None The aim of this dataset is to distinguish between nasal (class 0) an...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - Class (nominal)]}
{'MajorityClassSize': 3818.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 1586.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 5404.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
phoneme
[ "V1", "V2", "V3", "V4", "V5" ]
[ false, false, false, false, false ]
2,674
146,600
predictive_accuracy
accuracy_score
cylinder-bands
**Author**: Bob Evans, RR Donnelley & Sons Co. **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Cylinder+Bands) - August, 1995 **Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) ### Description Cylinder bands UCI dataset - Process delays known as cylinder banding...
{0: [0 - timestamp (nominal)], 1: [1 - cylinder_number (nominal)], 2: [2 - customer (nominal)], 3: [3 - job_number (numeric)], 4: [4 - grain_screened (nominal)], 5: [5 - ink_color (nominal)], 6: [6 - proof_on_ctd_ink (nominal)], 7: [7 - blade_mfg (nominal)], 8: [8 - cylinder_division (nominal)], 9: [9 - paper_...
{'MajorityClassSize': 312.0, 'MaxNominalAttDistinctValues': 71.0, 'MinorityClassSize': 228.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 40.0, 'NumberOfInstances': 540.0, 'NumberOfInstancesWithMissingValues': 263.0, 'NumberOfMissingValues': 999.0, 'NumberOfNumericFeatures': 18.0, 'NumberOfSymbolicFeatures': 2...
cylinder-bands
[ "customer", "job_number", "grain_screened", "ink_color", "proof_on_ctd_ink", "blade_mfg", "cylinder_division", "paper_type", "ink_type", "direct_steam", "solvent_type", "type_on_cylinder", "press_type", "press", "unit_number", "cylinder_size", "paper_mill_location", "plating_tank",...
[ true, false, true, true, true, true, true, true, true, true, true, true, true, true, false, true, true, true, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, true ]
2,675
168,786
predictive_accuracy
accuracy_score
car
**Author**: Marko Bohanec, Blaz Zupan **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/car+evaluation) - 1997 **Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) **Car Evaluation Database** This database was derived from a simple hierarchical decision model originally developed...
{0: [0 - buying (nominal)], 1: [1 - maint (nominal)], 2: [2 - doors (nominal)], 3: [3 - persons (nominal)], 4: [4 - lug_boot (nominal)], 5: [5 - safety (nominal)], 6: [6 - class (nominal)]}
{'MajorityClassSize': 1210.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 65.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 7.0, 'NumberOfInstances': 1728.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 7.0, '...
car
[ "buying", "maint", "doors", "persons", "lug_boot", "safety" ]
[ true, true, true, true, true, true ]
2,676
168,342
AUC
roc_auc_score
credit-g
**Author**: Dr. Hans Hofmann **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **German Credit dataset** This dataset classifies people described by a set of attributes as good or bad credit...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0,...
credit-g
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
2,677
168,757
predictive_accuracy
accuracy_score
credit-g
**Author**: Dr. Hans Hofmann **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **German Credit dataset** This dataset classifies people described by a set of attributes as good or bad credit...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0,...
credit-g
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
2,678
168,343
auc
roc_auc_score
credit-g
**Author**: Dr. Hans Hofmann **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **German Credit dataset** This dataset classifies people described by a set of attributes as good or bad credit...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0,...
credit-g
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
2,679
168,907
mean_absolute_error
mean_absolute_error
monks-problems-1
**Author**: Sebastian Thrun (Carnegie Mellon University) **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **The Monk's Problems: Problem 1** Once upon a time, in July 1991, the monks of Corsend...
{0: [0 - class (nominal)], 1: [1 - attr1 (nominal)], 2: [2 - attr2 (nominal)], 3: [3 - attr3 (nominal)], 4: [4 - attr4 (nominal)], 5: [5 - attr5 (nominal)], 6: [6 - attr6 (nominal)]}
{'MajorityClassSize': 278.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 278.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 7.0, 'NumberOfInstances': 556.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 7.0, 'c...
monks-problems-1
[ "attr1", "attr2", "attr3", "attr4", "attr5", "attr6" ]
[ true, true, true, true, true, true ]
2,680
167,214
area_under_roc_curve
roc_auc_score
segment
**Author**: University of Massachusetts Vision Group, Carla Brodley **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/image+segmentation) - 1990 **Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) **Image Segmentation Data Set** The instances were drawn randomly from a database of 7...
{0: [0 - region.centroid.col (numeric)], 1: [1 - region.centroid.row (numeric)], 2: [2 - region.pixel.count (numeric)], 3: [3 - short.line.density.5 (numeric)], 4: [4 - short.line.density.2 (numeric)], 5: [5 - vedge.mean (numeric)], 6: [6 - vegde.sd (numeric)], 7: [7 - hedge.mean (numeric)], 8: [8 - hedge.sd (n...
{'MajorityClassSize': 330.0, 'MaxNominalAttDistinctValues': 7.0, 'MinorityClassSize': 330.0, 'NumberOfClasses': 7.0, 'NumberOfFeatures': 20.0, 'NumberOfInstances': 2310.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 19.0, 'NumberOfSymbolicFeatures': 1.0, ...
segment
[ "short.line.density.5", "short.line.density.2", "vedge.mean", "vegde.sd", "hedge.mean", "hedge.sd", "intensity.mean", "rawred.mean", "rawblue.mean", "rawgreen.mean", "exred.mean", "exblue.mean", "exgreen.mean", "value.mean", "saturation.mean", "hue.mean" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,681
168,870
confusion_matrix,predictive_accuracy,area_under_roc_curve
roc_auc_score
DRSongsLyrics
This dataset contains 358 lyrics of songs for the rock bands 'The Rolling Stones' and 'Deep Purple'. The bands are equally represented in the dataset (179 songs for each band). This dataset was extracted from the much larger 'SongLyrics' dataset created by Sergey Kuznetsov.
{0: [0 - identifier (numeric)], 1: [1 - text (string)], 2: [2 - class (nominal)]}
{'MajorityClassSize': 179.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 179.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 2.0, 'NumberOfInstances': 358.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
DRSongsLyrics
[ "text" ]
[ false ]
2,682
10,092
predictive_accuracy
accuracy_score
arcene
**Author**: **Source**: UCI **Please cite**: ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge. Source: a. Origina...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 112.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 88.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10001.0, 'NumberOfInstances': 200.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10000.0, 'NumberOfSymbolicFeatures': 1...
arcene
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,683
146,604
predictive_accuracy
accuracy_score
texture
**Author**: Laboratory of Image Processing and Pattern Recognition (INPG-LTIRF), Grenoble - France. **Source**: [ELENA project](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/texture/) **Please cite**: None ####1. Summary This database was generated by the Laboratory of Image Proce...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 500.0, 'MaxNominalAttDistinctValues': 11.0, 'MinorityClassSize': 500.0, 'NumberOfClasses': 11.0, 'NumberOfFeatures': 41.0, 'NumberOfInstances': 5500.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 40.0, 'NumberOfSymbolicFeatures': 1.0...
texture
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,684
189,938
average_cost
accuracy_score
credit-g
**Author**: Dr. Hans Hofmann **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **German Credit dataset** This dataset classifies people described by a set of attributes as good or bad credit...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0,...
credit-g
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
2,685
4,701
predictive_accuracy
accuracy_score
AP_Ovary_Uterus
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1438_at (numeric)], 5: [5 - 1552256_a_at (numeric)], 6: [6 - 1552257_a_at (numeric)], 7: [7 - 1552261_at (numeric)], 8: [8 - 1552309_a_at (numeric)], 9: [9 - 1552348_at (numeric)], 10: [1...
{'MajorityClassSize': 198.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 124.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 322.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': ...
AP_Ovary_Uterus
[ "1007_s_at", "121_at", "1405_i_at", "1438_at", "1552256_a_at", "1552257_a_at", "1552261_at", "1552309_a_at", "1552348_at", "1552349_a_at", "1552365_at", "1552368_at", "1552378_s_at", "1552426_a_at", "1552477_a_at", "1552566_at", "1552594_at", "1552610_a_at", "1552619_a_at", "15...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,686
146,197
precision
precision_score
dna
**Author**: Ross King, based on data from Genbank 64.1 **Source**: [MLbench](https://www.rdocumentation.org/packages/mlbench/versions/2.1-1/topics/DNA). Originally from the StatLog project. **Please Cite**: **Primate Splice-Junction Gene Sequences (DNA)** Originally from the StatLog project. The raw data is s...
{0: [0 - A0 (nominal)], 1: [1 - A1 (nominal)], 2: [2 - A2 (nominal)], 3: [3 - A3 (nominal)], 4: [4 - A4 (nominal)], 5: [5 - A5 (nominal)], 6: [6 - A6 (nominal)], 7: [7 - A7 (nominal)], 8: [8 - A8 (nominal)], 9: [9 - A9 (nominal)], 10: [10 - A10 (nominal)], 11: [11 - A11 (nominal)], 12: [12 - A12 (nominal)],...
{'MajorityClassSize': 1654.0, 'MaxNominalAttDistinctValues': 3.0, 'MinorityClassSize': 765.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 181.0, 'NumberOfInstances': 3186.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 181....
dna
[ "A0", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "A22", "A23", "A24", "A25", "A26", "A27", "A28", "A29", "A30", "A31", "A32", "A33", "A34", "A35", "A...
[ true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true...
2,687
190,140
area_under_roc_curve
roc_auc_score
ilpd-numeric
The ILPD liver dataset from the OpenCC18 with the gender binary encoded so all features are numeric
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - Class (nominal)]}
{'MajorityClassSize': 416.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 167.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 583.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0, ...
ilpd-numeric
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10" ]
[ false, false, false, false, false, false, false, false, false, false ]
2,688
190,139
area_under_roc_curve
roc_auc_score
blood-transfusion-service-center
**Author**: Prof. I-Cheng Yeh **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center) **Please cite**: Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence", Expert Systems with Applications, 2008. **Blood Transfusion S...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - Class (nominal)]}
{'MajorityClassSize': 570.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 178.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 748.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
blood-transfusion-service-center
[ "V1", "V2", "V3", "V4" ]
[ false, false, false, false ]
2,689
190,136
confusion_matrix
accuracy_score
diabetes
**Author**: [Vincent Sigillito](vgs@aplcen.apl.jhu.edu) **Source**: [Obtained from UCI](https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes) **Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) 1. Title: Pima Indians Diabetes Database 2. Sources: (a) Origi...
{0: [0 - preg (numeric)], 1: [1 - plas (numeric)], 2: [2 - pres (numeric)], 3: [3 - skin (numeric)], 4: [4 - insu (numeric)], 5: [5 - mass (numeric)], 6: [6 - pedi (numeric)], 7: [7 - age (numeric)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 500.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 268.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 768.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
diabetes
[ "preg", "plas", "pres", "skin", "insu", "mass", "pedi", "age" ]
[ false, false, false, false, false, false, false, false ]
2,691
146,205
precision
precision_score
led7
led7-pmlb
{0: [0 - attribute#1 (nominal)], 1: [1 - attribute#2 (nominal)], 2: [2 - attribute#3 (nominal)], 3: [3 - attribute#4 (nominal)], 4: [4 - attribute#5 (nominal)], 5: [5 - attribute#6 (nominal)], 6: [6 - attribute#7 (nominal)], 7: [7 - class (nominal)]}
{'MajorityClassSize': 341.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 270.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 3200.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 8.0, ...
led7
[ "attribute#1", "attribute#2", "attribute#3", "attribute#4", "attribute#5", "attribute#6", "attribute#7" ]
[ true, true, true, true, true, true, true ]
2,692
190,145
average_cost
accuracy_score
MIP-2016-PAR10-classification
source: http://plato.asu.edu/ftp/solvable.html authors: Rolf-David Bergdoll PAR10 performances of modern solvers on the solvable instances of MIPLIB2010. http://miplib.zib.de/ The algorithm runtime data was directly taken from the '12 threads' table of H. Mittelmann's evaluations. The features were generated using t...
{0: [0 - probtype (numeric)], 1: [1 - n_vars (numeric)], 2: [2 - n_constr (numeric)], 3: [3 - n_nzcnt (numeric)], 4: [4 - nq_vars (numeric)], 5: [5 - nq_constr (numeric)], 6: [6 - nq_nzcnt (numeric)], 7: [7 - lp_avg (numeric)], 8: [8 - lp_l2_avg (numeric)], 9: [9 - lp_linf (numeric)], 10: [10 - lp_objval (num...
{'MajorityClassSize': 84.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 5.0, 'NumberOfFeatures': 144.0, 'NumberOfInstances': 218.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 143.0, 'NumberOfSymbolicFeatures': 1.0, '...
MIP-2016-PAR10-classification
[ "probtype", "n_vars", "n_constr", "n_nzcnt", "nq_vars", "nq_constr", "nq_nzcnt", "lp_avg", "lp_l2_avg", "lp_linf", "lp_objval", "num_b_variables", "num_i_variables", "num_c_variables", "num_s_variables", "num_n_variables", "ratio_b_variables", "ratio_i_variables", "ratio_c_variab...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,693
189,934
predictive_accuracy
accuracy_score
MIP-2016-classification
source: http://plato.asu.edu/ftp/solvable.html authors: Rolf-David Bergdoll PAR10 performances of modern solvers on the solvable instances of MIPLIB2010. http://miplib.zib.de/ The algorithm runtime data was directly taken from the '12 threads' table of H. Mittelmann's evaluations. The features were generated using t...
{0: [0 - instance_id (string)], 1: [1 - repetition (numeric)], 2: [2 - probtype (numeric)], 3: [3 - n_vars (numeric)], 4: [4 - n_constr (numeric)], 5: [5 - n_nzcnt (numeric)], 6: [6 - nq_vars (numeric)], 7: [7 - nq_constr (numeric)], 8: [8 - nq_nzcnt (numeric)], 9: [9 - lp_avg (numeric)], 10: [10 - lp_l2_avg ...
{'MajorityClassSize': 84.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 5.0, 'NumberOfFeatures': 148.0, 'NumberOfInstances': 218.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 145.0, 'NumberOfSymbolicFeatures': 2.0, '...
MIP-2016-classification
[ "instance_id", "repetition", "probtype", "n_vars", "n_constr", "n_nzcnt", "nq_vars", "nq_constr", "nq_nzcnt", "lp_avg", "lp_l2_avg", "lp_linf", "lp_objval", "num_b_variables", "num_i_variables", "num_c_variables", "num_s_variables", "num_n_variables", "ratio_b_variables", "rati...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,694
168,912
area_under_roc_curve
roc_auc_score
sylvine
SOURCE: [ChaLearn Automatic Machine Learning Challenge (AutoML)](https://competitions.codalab.org/competitions/2321), [ChaLearn](https://automl.chalearn.org/data) This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (thi...
{0: [0 - class (nominal)], 1: [1 - V1 (numeric)], 2: [2 - V2 (numeric)], 3: [3 - V3 (numeric)], 4: [4 - V4 (numeric)], 5: [5 - V5 (numeric)], 6: [6 - V6 (numeric)], 7: [7 - V7 (numeric)], 8: [8 - V8 (numeric)], 9: [9 - V9 (numeric)], 10: [10 - V10 (numeric)], 11: [11 - V11 (numeric)], 12: [12 - V12 (numeric...
{'MajorityClassSize': 2562.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 2562.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 5124.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
sylvine
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,695
211,831
c_index
roc_auc_score
iris
**Author**: R.A. Fisher **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall **Please cite**: **Iris Plants Database** This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is ref...
{0: [0 - sepallength (numeric)], 1: [1 - sepalwidth (numeric)], 2: [2 - petallength (numeric)], 3: [3 - petalwidth (numeric)], 4: [4 - class (nominal)]}
{'MajorityClassSize': 50.0, 'MaxNominalAttDistinctValues': 3.0, 'MinorityClassSize': 50.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 150.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'cos...
iris
[ "sepallength", "sepalwidth", "petallength", "petalwidth" ]
[ false, false, false, false ]
2,697
168,787
predictive_accuracy
accuracy_score
segment
**Author**: University of Massachusetts Vision Group, Carla Brodley **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/image+segmentation) - 1990 **Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) **Image Segmentation Data Set** The instances were drawn randomly from a database of 7...
{0: [0 - region.centroid.col (numeric)], 1: [1 - region.centroid.row (numeric)], 2: [2 - region.pixel.count (numeric)], 3: [3 - short.line.density.5 (numeric)], 4: [4 - short.line.density.2 (numeric)], 5: [5 - vedge.mean (numeric)], 6: [6 - vegde.sd (numeric)], 7: [7 - hedge.mean (numeric)], 8: [8 - hedge.sd (n...
{'MajorityClassSize': 330.0, 'MaxNominalAttDistinctValues': 7.0, 'MinorityClassSize': 330.0, 'NumberOfClasses': 7.0, 'NumberOfFeatures': 20.0, 'NumberOfInstances': 2310.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 19.0, 'NumberOfSymbolicFeatures': 1.0, ...
segment
[ "short.line.density.5", "short.line.density.2", "vedge.mean", "vegde.sd", "hedge.mean", "hedge.sd", "intensity.mean", "rawred.mean", "rawblue.mean", "rawgreen.mean", "exred.mean", "exblue.mean", "exgreen.mean", "value.mean", "saturation.mean", "hue.mean" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,699
75,228
predictive_accuracy
accuracy_score
mfeat-factors
**Author**: **Source**: Unknown - Date unknown **Please cite**: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte...
{0: [0 - att1 (numeric)], 1: [1 - att2 (numeric)], 2: [2 - att3 (numeric)], 3: [3 - att4 (numeric)], 4: [4 - att5 (numeric)], 5: [5 - att6 (numeric)], 6: [6 - att7 (numeric)], 7: [7 - att8 (numeric)], 8: [8 - att9 (numeric)], 9: [9 - att10 (numeric)], 10: [10 - att11 (numeric)], 11: [11 - att12 (numeric)], ...
{'MajorityClassSize': 1800.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 200.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 217.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 216.0, 'NumberOfSymbolicFeatures': 1....
mfeat-factors
[ "att1", "att2", "att3", "att4", "att5", "att6", "att7", "att8", "att9", "att10", "att11", "att12", "att13", "att14", "att15", "att16", "att17", "att18", "att19", "att20", "att21", "att22", "att23", "att24", "att25", "att26", "att27", "att28", "att29", "att30...
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2,700
190,138
average_cost
accuracy_score
CPMP-2015-runtime-classification
source: An Algorithm Selection Benchmark for the Container Pre-Marshalling Problem (CPMP) authors: K. Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms) translator in coseal format: K. Tierney This is an extension of the 2013 premarshalling dataset that includes more features and a ...
{0: [0 - stacks (numeric)], 1: [1 - tiers (numeric)], 2: [2 - stack.tier.ratio (numeric)], 3: [3 - container.density (numeric)], 4: [4 - empty.stack.pct (numeric)], 5: [5 - overstowing.stack.pct (numeric)], 6: [6 - overstowing.2cont.stack.pct (numeric)], 7: [7 - group.same.min (numeric)], 8: [8 - group.same.max...
{'MajorityClassSize': 208.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 78.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 23.0, 'NumberOfInstances': 527.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 22.0, 'NumberOfSymbolicFeatures': 1.0, '...
CPMP-2015-runtime-classification
[ "stacks", "tiers", "stack.tier.ratio", "container.density", "empty.stack.pct", "overstowing.stack.pct", "overstowing.2cont.stack.pct", "group.same.min", "group.same.max", "group.same.mean", "group.same.stdev", "top.good.min", "top.good.max", "top.good.mean", "top.good.stdev", "overstow...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,701
190,146
predictive_accuracy
accuracy_score
vehicle
**Author**: Dr. Pete Mowforth and Dr. Barry Shepherd **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes)) **Please cite**: Siebert,JP. Turing Institute Research Memorandum TIRM-87-018 "Vehicle Recognition Using Rule Based Methods" (March 1987) NAME vehicle silhouettes ...
{0: [0 - COMPACTNESS (numeric)], 1: [1 - CIRCULARITY (numeric)], 2: [2 - DISTANCE_CIRCULARITY (numeric)], 3: [3 - RADIUS_RATIO (numeric)], 4: [4 - PR.AXIS_ASPECT_RATIO (numeric)], 5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)], 6: [6 - SCATTER_RATIO (numeric)], 7: [7 - ELONGATEDNESS (numeric)], 8: [8 - PR.AXIS_RECT...
{'MajorityClassSize': 218.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 199.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 19.0, 'NumberOfInstances': 846.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 18.0, 'NumberOfSymbolicFeatures': 1.0, ...
vehicle
[ "COMPACTNESS", "CIRCULARITY", "DISTANCE_CIRCULARITY", "RADIUS_RATIO", "PR.AXIS_ASPECT_RATIO", "MAX.LENGTH_ASPECT_RATIO", "SCATTER_RATIO", "ELONGATEDNESS", "PR.AXIS_RECTANGULARITY", "MAX.LENGTH_RECTANGULARITY", "SCALED_VARIANCE_MAJOR", "SCALED_VARIANCE_MINOR", "SCALED_RADIUS_OF_GYRATION", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,702
146,592
predictive_accuracy
accuracy_score
hill-valley
**Author**: Lee Graham, Franz Oppacher **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/hill-valley) **Please cite**: Each record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y coordinate, the points will create either a Hill (a “bump” in the terrai...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 606.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 606.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 101.0, 'NumberOfInstances': 1212.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 100.0, 'NumberOfSymbolicFeatures': 1.0...
hill-valley
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,703
190,403
predictive_accuracy
accuracy_score
regime_alimentaire
#modelage
{0: [0 - Sexe (nominal)], 1: [1 - Origine_Ethnique (nominal)], 2: [2 - Age (numeric)], 3: [3 - Poids (numeric)], 4: [4 - Taille (numeric)], 5: [5 - pain_biscottes_cereales (nominal)], 6: [6 - riz_pates_semoule_pommes_de_terre (nominal)], 7: [7 - legumes_secs (nominal)], 8: [8 - legumes (nominal)], 9: [9 - frui...
{'MajorityClassSize': 161.0, 'MaxNominalAttDistinctValues': 7.0, 'MinorityClassSize': 41.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 20.0, 'NumberOfInstances': 202.0, 'NumberOfInstancesWithMissingValues': 17.0, 'NumberOfMissingValues': 17.0, 'NumberOfNumericFeatures': 3.0, 'NumberOfSymbolicFeatures': 17.0, ...
regime_alimentaire
[ "Sexe", "Origine_Ethnique", "Age", "Poids", "Taille", "pain_biscottes_cereales", "riz_pates_semoule_pommes_de_terre", "legumes_secs", "legumes", "fruits", "produits_laitiers", "viande_ou_oeufs", "poisson", "plats_prets_a_consommer", "produits_sucres", "biscuits_aperitif", "complement...
[ true, true, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true ]
2,704
190,153
average_cost
accuracy_score
SAT11-HAND-runtime-classification
source: http://www.cs.ubc.ca/labs/beta/Projects/SATzilla/ authors: L. Xu, F. Hutter, H. Hoos, K. Leyton-Brown translator in coseal format: M. Lindauer with the help of Alexandre Frechette the data do not distinguish between timeout, memout or crashes! the status file will only have ok or timeout! If features are "?", t...
{0: [0 - nvarsOrig (numeric)], 1: [1 - nclausesOrig (numeric)], 2: [2 - nvars (numeric)], 3: [3 - nclauses (numeric)], 4: [4 - reducedVars (numeric)], 5: [5 - reducedClauses (numeric)], 6: [6 - vars_clauses_ratio (numeric)], 7: [7 - POSNEG_RATIO_CLAUSE_mean (numeric)], 8: [8 - POSNEG_RATIO_CLAUSE_coeff_variatio...
{'MajorityClassSize': 91.0, 'MaxNominalAttDistinctValues': 14.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 14.0, 'NumberOfFeatures': 116.0, 'NumberOfInstances': 296.0, 'NumberOfInstancesWithMissingValues': 181.0, 'NumberOfMissingValues': 1810.0, 'NumberOfNumericFeatures': 115.0, 'NumberOfSymbolicFeatures': ...
SAT11-HAND-runtime-classification
[ "nvarsOrig", "nclausesOrig", "nvars", "nclauses", "reducedVars", "reducedClauses", "vars_clauses_ratio", "POSNEG_RATIO_CLAUSE_mean", "POSNEG_RATIO_CLAUSE_coeff_variation", "POSNEG_RATIO_CLAUSE_min", "POSNEG_RATIO_CLAUSE_max", "POSNEG_RATIO_CLAUSE_entropy", "VCG_CLAUSE_mean", "VCG_CLAUSE_co...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,705
189,932
predictive_accuracy
accuracy_score
CPMP-2015-classification
source: An Algorithm Selection Benchmark for the Container Pre-Marshalling Problem (CPMP) authors: K. Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms) translator in coseal format: K. Tierney This is an extension of the 2013 premarshalling dataset that includes more features and a ...
{0: [0 - instance_id (string)], 1: [1 - repetition (numeric)], 2: [2 - stacks (numeric)], 3: [3 - tiers (numeric)], 4: [4 - stack.tier.ratio (numeric)], 5: [5 - container.density (numeric)], 6: [6 - empty.stack.pct (numeric)], 7: [7 - overstowing.stack.pct (numeric)], 8: [8 - overstowing.2cont.stack.pct (numeri...
{'MajorityClassSize': 208.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 78.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 27.0, 'NumberOfInstances': 527.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 2.0, '...
CPMP-2015-classification
[ "instance_id", "repetition", "stacks", "tiers", "stack.tier.ratio", "container.density", "empty.stack.pct", "overstowing.stack.pct", "overstowing.2cont.stack.pct", "group.same.min", "group.same.max", "group.same.mean", "group.same.stdev", "top.good.min", "top.good.max", "top.good.mean"...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true ]
2,706
190,406
predictive_accuracy
accuracy_score
epiparo_extract
#modelage
{0: [0 - Sexe (nominal)], 1: [1 - Age (numeric)], 2: [2 - Poids (numeric)], 3: [3 - Tabac (nominal)], 4: [4 - Pathologies (nominal)], 5: [5 - Aliments_Sucres (nominal)], 6: [6 - Gras_Sale (nominal)], 7: [7 - Soda (nominal)], 8: [8 - Alcool (nominal)], 9: [9 - Frequence_RDV_CD (nominal)], 10: [10 - Hygiene_BD ...
{'MajorityClassSize': 123.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 20.0, 'NumberOfInstances': 224.0, 'NumberOfInstancesWithMissingValues': 143.0, 'NumberOfMissingValues': 205.0, 'NumberOfNumericFeatures': 9.0, 'NumberOfSymbolicFeatures': 11.0,...
epiparo_extract
[ "Sexe", "Age", "Poids", "Tabac", "Pathologies", "Aliments_Sucres", "Gras_Sale", "Soda", "Alcool", "Frequence_RDV_CD", "Hygiene_BD", "Stress_turel", "Dents_Absentes", "Dents_Carie_Obture", "GI", "REC", "PI", "Detartrage_Necessaire", "Diagnostic" ]
[ true, false, false, true, true, true, true, true, true, true, false, false, false, false, false, false, false, true, true ]
2,707
190,402
predictive_accuracy
accuracy_score
stress
#modelage
{0: [0 - Sexe (nominal)], 1: [1 - Age (numeric)], 2: [2 - Poids (numeric)], 3: [3 - Taille (numeric)], 4: [4 - Consommation_tabac (nominal)], 5: [5 - type_consommation (nominal)], 6: [6 - Allergies (nominal)], 7: [7 - stresse_a_cet_instant (numeric)], 8: [8 - nature_stressee (numeric)], 9: [9 - pression_au_tra...
{'MajorityClassSize': 159.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 3.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 202.0, 'NumberOfInstancesWithMissingValues': 172.0, 'NumberOfMissingValues': 202.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 5.0, ...
stress
[ "Sexe", "Age", "Poids", "Taille", "Consommation_tabac", "type_consommation", "Allergies", "stresse_a_cet_instant", "nature_stressee", "pression_au_travail", "evenements_stressant_derniers_mois", "Stress_dernier_mois" ]
[ true, false, false, false, true, true, true, false, false, false, false, false ]
2,708
190,404
predictive_accuracy
accuracy_score
epiparo_extract
#modelage
{0: [0 - Sexe (nominal)], 1: [1 - Age (numeric)], 2: [2 - Poids (numeric)], 3: [3 - Tabac (nominal)], 4: [4 - Pathologies (nominal)], 5: [5 - Aliments_Sucres (nominal)], 6: [6 - Gras_Sale (nominal)], 7: [7 - Soda (nominal)], 8: [8 - Alcool (nominal)], 9: [9 - Frequence_RDV_CD (nominal)], 10: [10 - Hygiene_BD ...
{'MajorityClassSize': 123.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 20.0, 'NumberOfInstances': 224.0, 'NumberOfInstancesWithMissingValues': 143.0, 'NumberOfMissingValues': 205.0, 'NumberOfNumericFeatures': 9.0, 'NumberOfSymbolicFeatures': 11.0,...
epiparo_extract
[ "Sexe", "Age", "Poids", "Tabac", "Pathologies", "Aliments_Sucres", "Gras_Sale", "Soda", "Alcool", "Frequence_RDV_CD", "Hygiene_BD", "Stress_turel", "Dents_Absentes", "Dents_Carie_Obture", "GI", "REC", "PI", "Gingivorragies", "Detartrage_Necessaire" ]
[ true, false, false, true, true, true, true, true, true, true, false, false, false, false, false, false, false, true, true ]
2,709
167,211
area_under_roc_curve
roc_auc_score
Satellite
**Author**: Markus Goldstein **Source**: [Dataverse](http://www.madm.eu/downloads https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF) **Please cite**: The satellite dataset comprises of features extracted from satellite observations. In particular, each image was taken under four di...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 5025.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 75.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 37.0, 'NumberOfInstances': 5100.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 36.0, 'NumberOfSymbolicFeatures': 1.0, ...
Satellite
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,710
189,937
average_cost
accuracy_score
CPMP-2015-classification
source: An Algorithm Selection Benchmark for the Container Pre-Marshalling Problem (CPMP) authors: K. Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms) translator in coseal format: K. Tierney This is an extension of the 2013 premarshalling dataset that includes more features and a ...
{0: [0 - instance_id (string)], 1: [1 - repetition (numeric)], 2: [2 - stacks (numeric)], 3: [3 - tiers (numeric)], 4: [4 - stack.tier.ratio (numeric)], 5: [5 - container.density (numeric)], 6: [6 - empty.stack.pct (numeric)], 7: [7 - overstowing.stack.pct (numeric)], 8: [8 - overstowing.2cont.stack.pct (numeri...
{'MajorityClassSize': 208.0, 'MaxNominalAttDistinctValues': 4.0, 'MinorityClassSize': 78.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 27.0, 'NumberOfInstances': 527.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 2.0, '...
CPMP-2015-classification
[ "instance_id", "repetition", "stacks", "tiers", "stack.tier.ratio", "container.density", "empty.stack.pct", "overstowing.stack.pct", "overstowing.2cont.stack.pct", "group.same.min", "group.same.max", "group.same.mean", "group.same.stdev", "top.good.min", "top.good.max", "top.good.mean"...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true ]
2,711
146,601
predictive_accuracy
accuracy_score
cjs
**Author**: Dr. Fernando Camacho **Source**: Unknown - 1995 **Please cite**: Camacho, F. and Arron, G. (1995) Effects of the regulators paclobutrazol and flurprimidol on the growth of terminal sprouts formed on trimmed silver maple trees. Canadian Journal of Statistics 3(23). Data on tree growth used in the Case ...
{0: [0 - N (numeric)], 1: [1 - TR (nominal)], 2: [2 - TREE (nominal)], 3: [3 - BR (nominal)], 4: [4 - TL (numeric)], 5: [5 - IN (numeric)], 6: [6 - INTERNODE_1 (numeric)], 7: [7 - INTERNODE_2 (numeric)], 8: [8 - INTERNODE_3 (numeric)], 9: [9 - INTERNODE_4 (numeric)], 10: [10 - INTERNODE_5 (numeric)], 11: [11...
{'MajorityClassSize': 680.0, 'MaxNominalAttDistinctValues': 57.0, 'MinorityClassSize': 274.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 35.0, 'NumberOfInstances': 2796.0, 'NumberOfInstancesWithMissingValues': 2795.0, 'NumberOfMissingValues': 68100.0, 'NumberOfNumericFeatures': 32.0, 'NumberOfSymbolicFeatures...
cjs
[ "TREE", "BR", "TL", "IN", "INTERNODE_1", "INTERNODE_2", "INTERNODE_3", "INTERNODE_4", "INTERNODE_5", "INTERNODE_6", "INTERNODE_7", "INTERNODE_8", "INTERNODE_9", "INTERNODE_10", "INTERNODE_11", "INTERNODE_12", "INTERNODE_13", "INTERNODE_14", "INTERNODE_15", "INTERNODE_16", "IN...
[ true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
2,712
4,696
predictive_accuracy
accuracy_score
AP_Endometrium_Kidney
**Author**: **Source**: Unknown - Date unknown **Please cite**: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC...
{0: [0 - ID_REF (numeric)], 1: [1 - 1007_s_at (numeric)], 2: [2 - 121_at (numeric)], 3: [3 - 1405_i_at (numeric)], 4: [4 - 1487_at (numeric)], 5: [5 - 1552256_a_at (numeric)], 6: [6 - 1552274_at (numeric)], 7: [7 - 1552275_s_at (numeric)], 8: [8 - 1552309_a_at (numeric)], 9: [9 - 1552348_at (numeric)], 10: [1...
{'MajorityClassSize': 260.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 61.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 10936.0, 'NumberOfInstances': 321.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10935.0, 'NumberOfSymbolicFeatures': 1...
AP_Endometrium_Kidney
[ "1007_s_at", "121_at", "1405_i_at", "1487_at", "1552256_a_at", "1552274_at", "1552275_s_at", "1552309_a_at", "1552348_at", "1552362_a_at", "1552365_at", "1552367_a_at", "1552426_a_at", "1552455_at", "1552509_a_at", "1552519_at", "1552594_at", "1552610_a_at", "1552615_at", "1552...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,713
211,241
confusion_matrix
accuracy_score
iris
**Author**: R.A. Fisher **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall **Please cite**: **Iris Plants Database** This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is ref...
{0: [0 - sepallength (numeric)], 1: [1 - sepalwidth (numeric)], 2: [2 - petallength (numeric)], 3: [3 - petalwidth (numeric)], 4: [4 - class (nominal)]}
{'MajorityClassSize': 50.0, 'MaxNominalAttDistinctValues': 3.0, 'MinorityClassSize': 50.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 5.0, 'NumberOfInstances': 150.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 4.0, 'NumberOfSymbolicFeatures': 1.0, 'cos...
iris
[ "sepallength", "sepalwidth", "petallength", "petalwidth" ]
[ false, false, false, false ]
2,714
145,960
precision
precision_score
mfeat-factors
**Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **Multiple Features Dataset: Factors** One of a set of 6 d...
{0: [0 - att1 (numeric)], 1: [1 - att2 (numeric)], 2: [2 - att3 (numeric)], 3: [3 - att4 (numeric)], 4: [4 - att5 (numeric)], 5: [5 - att6 (numeric)], 6: [6 - att7 (numeric)], 7: [7 - att8 (numeric)], 8: [8 - att9 (numeric)], 9: [9 - att10 (numeric)], 10: [10 - att11 (numeric)], 11: [11 - att12 (numeric)], ...
{'MajorityClassSize': 200.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 200.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 217.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 216.0, 'NumberOfSymbolicFeatures': 1...
mfeat-factors
[ "att1", "att2", "att3", "att4", "att5", "att6", "att7", "att8", "att9", "att10", "att11", "att12", "att13", "att14", "att15", "att16", "att17", "att18", "att19", "att20", "att21", "att22", "att23", "att24", "att25", "att26", "att27", "att28", "att29", "att30...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,715
211,709
mean_precision
precision_score
Nishant
good
{0: [0 - Country (nominal)], 1: [1 - Age (numeric)], 2: [2 - Salary (numeric)], 3: [3 - Purchased (nominal)]}
{'MajorityClassSize': nan, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': nan, 'NumberOfClasses': nan, 'NumberOfFeatures': 4.0, 'NumberOfInstances': 10.0, 'NumberOfInstancesWithMissingValues': 2.0, 'NumberOfMissingValues': 2.0, 'NumberOfNumericFeatures': 2.0, 'NumberOfSymbolicFeatures': 2.0, 'cost_m...
Nishant
[ "Age", "Salary", "Purchased" ]
[ false, false, true ]
2,716
190,405
predictive_accuracy
accuracy_score
epiparo_extract
#modelage
{0: [0 - Sexe (nominal)], 1: [1 - Age (numeric)], 2: [2 - Poids (numeric)], 3: [3 - Tabac (nominal)], 4: [4 - Pathologies (nominal)], 5: [5 - Aliments_Sucres (nominal)], 6: [6 - Gras_Sale (nominal)], 7: [7 - Soda (nominal)], 8: [8 - Alcool (nominal)], 9: [9 - Frequence_RDV_CD (nominal)], 10: [10 - Hygiene_BD ...
{'MajorityClassSize': 123.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 1.0, 'NumberOfClasses': 6.0, 'NumberOfFeatures': 20.0, 'NumberOfInstances': 224.0, 'NumberOfInstancesWithMissingValues': 143.0, 'NumberOfMissingValues': 205.0, 'NumberOfNumericFeatures': 9.0, 'NumberOfSymbolicFeatures': 11.0,...
epiparo_extract
[ "Sexe", "Age", "Poids", "Tabac", "Pathologies", "Aliments_Sucres", "Gras_Sale", "Soda", "Alcool", "Frequence_RDV_CD", "Hygiene_BD", "Stress_turel", "Dents_Absentes", "Dents_Carie_Obture", "GI", "REC", "PI", "Gingivorragies", "Diagnostic" ]
[ true, false, false, true, true, true, true, true, true, true, false, false, false, false, false, false, false, true, true ]
2,717
167,123
predictive_accuracy
accuracy_score
one-hundred-plants-margin
**Author**: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set) - 2010 **Please cite**: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture a...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 16.0, 'MaxNominalAttDistinctValues': 100.0, 'MinorityClassSize': 16.0, 'NumberOfClasses': 100.0, 'NumberOfFeatures': 65.0, 'NumberOfInstances': 1600.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 64.0, 'NumberOfSymbolicFeatures': 1.0...
one-hundred-plants-margin
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,718
190,424
predictive_accuracy
accuracy_score
compas-two-years
nominal features and target for COMPAS
{0: [0 - sex (nominal)], 1: [1 - age (numeric)], 2: [2 - juv_fel_count (numeric)], 3: [3 - juv_misd_count (numeric)], 4: [4 - juv_other_count (numeric)], 5: [5 - priors_count (numeric)], 6: [6 - two_year_recid (nominal)], 7: [7 - age_cat_25-45 (nominal)], 8: [8 - age_cat_Greaterthan45 (nominal)], 9: [9 - age_c...
{'MajorityClassSize': 2795.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 2483.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 14.0, 'NumberOfInstances': 5278.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 7.0,...
compas-two-years
[ "sex", "age", "juv_fel_count", "juv_misd_count", "juv_other_count", "priors_count", "age_cat_25-45", "age_cat_Greaterthan45", "age_cat_Lessthan25", "race_African-American", "race_Caucasian", "c_charge_degree_F", "c_charge_degree_M" ]
[ true, false, false, false, false, false, true, true, true, true, true, false, false ]
2,719
190,144
predictive_accuracy
accuracy_score
cnae-9
**Author**: **Source**: Unknown - Date unknown **Please cite**: Binarized version of the cnae-9 dataset (see version 1). Only instances with class labels 1 and 2 from the original dataset are considered.
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 120.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 120.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 857.0, 'NumberOfInstances': 240.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 856.0, 'NumberOfSymbolicFeatures': 1.0,...
cnae-9
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,720
4,629
predictive_accuracy
accuracy_score
rsctc2010_1
**Author**: **Source**: Unknown - Date unknown **Please cite**: Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar...
{0: [0 - Var1 (numeric)], 1: [1 - Var2 (numeric)], 2: [2 - Var3 (numeric)], 3: [3 - Var4 (numeric)], 4: [4 - Var5 (numeric)], 5: [5 - Var6 (numeric)], 6: [6 - Var7 (numeric)], 7: [7 - Var8 (numeric)], 8: [8 - Var9 (numeric)], 9: [9 - Var10 (numeric)], 10: [10 - Var11 (numeric)], 11: [11 - Var12 (numeric)], ...
{'MajorityClassSize': 58.0, 'MaxNominalAttDistinctValues': 3.0, 'MinorityClassSize': 7.0, 'NumberOfClasses': 3.0, 'NumberOfFeatures': 22284.0, 'NumberOfInstances': 105.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 22283.0, 'NumberOfSymbolicFeatures': 1.0...
rsctc2010_1
[ "Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7", "Var8", "Var9", "Var10", "Var11", "Var12", "Var13", "Var14", "Var15", "Var16", "Var17", "Var18", "Var19", "Var20", "Var21", "Var22", "Var23", "Var24", "Var25", "Var26", "Var27", "Var28", "Var29", "Var30...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
2,721
146,213
precision
precision_score
solar-flare
**Author**: Gary Bradshaw **Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/solar+flare) **Please cite**: **Solar Flare database** Relevant Information: -- The database contains 3 potential classes, one for the number of times a certain type of solar flare occured in a 24 hour period. -...
{0: [0 - largest_spot_size (nominal)], 1: [1 - spot_distribution (nominal)], 2: [2 - Activity (nominal)], 3: [3 - Evolution (nominal)], 4: [4 - Previous_24_hour_flare_activity_code (nominal)], 5: [5 - Historically-complex (nominal)], 6: [6 - Did_region_become_historically_complex (nominal)], 7: [7 - Area (nomina...
{'MajorityClassSize': 88.0, 'MaxNominalAttDistinctValues': 6.0, 'MinorityClassSize': 21.0, 'NumberOfClasses': 5.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 315.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 0.0, 'NumberOfSymbolicFeatures': 13.0, 'c...
solar-flare
[ "largest_spot_size", "spot_distribution", "Activity", "Evolution", "Previous_24_hour_flare_activity_code", "Historically-complex", "Did_region_become_historically_complex", "Area", "Area_of_the_largest_spot", "C-class_flares_production_by_this_region", "M-class_flares_production_by_this_region",...
[ true, true, true, true, true, true, true, true, true, true, true, true ]
2,722
233,088
predictive_accuracy
accuracy_score
credit-g
**Author**: Dr. Hans Hofmann **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 **Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) **German Credit dataset** This dataset classifies people described by a set of attributes as good or bad credit...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': 10.0, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0,...
credit-g
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
2,723
4,186
predictive_accuracy
accuracy_score
isolet
**Author**: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu) **Donor**: Tom Dietterich (tgd@cs.orst.edu) **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ISOLET) **Please cite**: UCI ### Description ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjec...
{0: [0 - f1 (numeric)], 1: [1 - f2 (numeric)], 2: [2 - f3 (numeric)], 3: [3 - f4 (numeric)], 4: [4 - f5 (numeric)], 5: [5 - f6 (numeric)], 6: [6 - f7 (numeric)], 7: [7 - f8 (numeric)], 8: [8 - f9 (numeric)], 9: [9 - f10 (numeric)], 10: [10 - f11 (numeric)], 11: [11 - f12 (numeric)], 12: [12 - f13 (numeric)]...
{'MajorityClassSize': 300.0, 'MaxNominalAttDistinctValues': 26.0, 'MinorityClassSize': 298.0, 'NumberOfClasses': 26.0, 'NumberOfFeatures': 618.0, 'NumberOfInstances': 7797.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 617.0, 'NumberOfSymbolicFeatures': 1...
isolet
[ "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "f10", "f11", "f12", "f13", "f14", "f15", "f16", "f17", "f18", "f19", "f20", "f21", "f22", "f23", "f24", "f25", "f26", "f27", "f28", "f29", "f30", "f31", "f32", "f33", "f34", "f35", "f36", "...
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2,724