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5,238 | mean_absolute_error | mean_absolute_error | fri_c4_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
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5,234 | mean_absolute_error | mean_absolute_error | fri_c1_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
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5,210 | mean_absolute_error | mean_absolute_error | fri_c4_1000_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
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5,237 | mean_absolute_error | mean_absolute_error | fri_c4_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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211,750 | predictive_accuracy | accuracy_score | la1s.wc | null | {0: [0 - aa (numeric)],
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5,246 | mean_absolute_error | mean_absolute_error | fri_c0_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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5,253 | mean_absolute_error | mean_absolute_error | fri_c1_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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'cost_... | fri_c1_500_5 | [
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5,256 | mean_absolute_error | mean_absolute_error | fri_c2_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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'cos... | fri_c2_100_10 | [
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5,251 | mean_absolute_error | mean_absolute_error | fri_c1_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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5,261 | mean_absolute_error | mean_absolute_error | fri_c3_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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'cos... | fri_c3_100_25 | [
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5,247 | mean_absolute_error | mean_absolute_error | fri_c4_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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5,250 | mean_absolute_error | mean_absolute_error | fri_c3_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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2: [2 - oz3 (numeric)],
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'cost... | fri_c3_1000_5 | [
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5,245 | mean_absolute_error | mean_absolute_error | fri_c4_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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'co... | fri_c4_1000_10 | [
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5,258 | mean_absolute_error | mean_absolute_error | fri_c1_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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7,573 | predictive_accuracy | accuracy_score | duke-breast-cancer | **Author**: Shirish Krishnaj Shevade and S. Sathiya Keerthi.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: Shirish Krishnaj Shevade and S. Sathiya Keerthi.
A simple and efficient algorithm for gene selection using spar... | {0: [0 - att_1 (numeric)],
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'... | duke-breast-cancer | [
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f... | 629 |
5,255 | mean_absolute_error | mean_absolute_error | fri_c0_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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5,257 | mean_absolute_error | mean_absolute_error | fri_c0_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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'cos... | fri_c0_250_10 | [
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] | [
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5,248 | mean_absolute_error | mean_absolute_error | fri_c2_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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12: [12 - oz... | {'MajorityClassSize': nan,
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'cos... | fri_c2_500_50 | [
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f... | 632 |
5,032 | mean_absolute_error | mean_absolute_error | MercuryinBass | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand.
Source: TunedIT: http://tunedit.org/repo/DASL
DASL file http://lib.stat.cmu.edu/DASL/Datafiles/MercuryinBass... | {0: [0 - ID (numeric)],
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6: [6 - Avg_Mercury (numeric)],
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9: [9 - max (numeric)],
10: [10 - 3_yr_Standard_Mercury (num... | {'MajorityClassSize': nan,
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'cost... | MercuryinBass | [
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5,254 | mean_absolute_error | mean_absolute_error | fri_c3_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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'cos... | fri_c3_250_50 | [
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5,252 | mean_absolute_error | mean_absolute_error | fri_c2_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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f... | 635 |
5,264 | mean_absolute_error | mean_absolute_error | fri_c4_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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12: [12 - oz... | {'MajorityClassSize': nan,
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'cos... | fri_c4_100_50 | [
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f... | 636 |
5,260 | mean_absolute_error | mean_absolute_error | fri_c2_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
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5,263 | mean_absolute_error | mean_absolute_error | fri_c1_500_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
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10: [10 - oz11 (numeric)]} | {'MajorityClassSize': nan,
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'cos... | fri_c1_500_10 | [
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211,747 | predictive_accuracy | accuracy_score | la2s.wc | null | {0: [0 - aa (numeric)],
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7: [7 - abbe (numeric)],
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10: [10 - abdomen (numeric)],
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... | {'MajorityClassSize': 905.0,
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'NumberOfSymbolicFeatures':... | la2s.wc | [
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5,267 | mean_absolute_error | mean_absolute_error | fri_c3_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
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'cos... | fri_c3_500_50 | [
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5,244 | mean_absolute_error | mean_absolute_error | fri_c2_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
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8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
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'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | fri_c2_1000_50 | [
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f... | 641 |
5,269 | mean_absolute_error | mean_absolute_error | fri_c1_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
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6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)]} | {'MajorityClassSize': nan,
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'cos... | fri_c1_250_10 | [
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] | [
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false,
false,
false,
false,
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] | 642 |
5,265 | mean_absolute_error | mean_absolute_error | fri_c2_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
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6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
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'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c2_500_25 | [
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] | 643 |
5,259 | mean_absolute_error | mean_absolute_error | fri_c1_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c1_500_50 | [
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f... | 644 |
5,266 | mean_absolute_error | mean_absolute_error | fri_c4_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
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6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
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'NumberOfClasses': 0.0,
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'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c4_250_25 | [
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] | 645 |
5,268 | mean_absolute_error | mean_absolute_error | fri_c3_500_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c3_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 646 |
5,242 | mean_absolute_error | mean_absolute_error | fri_c1_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
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6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | fri_c1_1000_25 | [
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"oz20",
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] | [
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false,
false,
false,
false,
false,
false,
false,
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] | 647 |
5,262 | mean_absolute_error | mean_absolute_error | fri_c4_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c4_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 648 |
5,082 | mean_absolute_error | mean_absolute_error | mtp | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode).
The molecules and outputs are taken from the original studies (see below). The other... | {0: [0 - oz1 (numeric)],
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6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 203.0,
'NumberOfInstances': 4450.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 203.0,
'NumberOfSymbolicFeatures': 0.0,
'... | mtp | [
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f... | 649 |
5,270 | mean_absolute_error | mean_absolute_error | fri_c1_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c1_250_50 | [
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"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
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"oz33... | [
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f... | 650 |
5,240 | mean_absolute_error | mean_absolute_error | fri_c3_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | fri_c3_1000_50 | [
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f... | 651 |
5,271 | mean_absolute_error | mean_absolute_error | fri_c0_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | fri_c0_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 652 |
5,229 | mean_absolute_error | mean_absolute_error | fri_c4_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | fri_c4_1000_50 | [
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"oz3",
"oz4",
"oz5",
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"oz26",
"oz27",
"oz28",
"oz29",
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"oz33... | [
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f... | 653 |
5,274 | mean_absolute_error | mean_absolute_error | fri_c4_100_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 101.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | fri_c4_100_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
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f... | 654 |
5,278 | mean_absolute_error | mean_absolute_error | fri_c1_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | fri_c1_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 655 |
5,273 | mean_absolute_error | mean_absolute_error | fri_c0_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c0_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 656 |
5,284 | mean_absolute_error | mean_absolute_error | rabe_266 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This file contains data from Regression Analysis By Example, 2nd Edition,
by Samprit Chatterjee and Bertram Price, John Wiley, 1991.
Data sets have names of the form 'rabe.xxx' where xxx is the page number
in the book where the data occurs.
For a... | {0: [0 - col_1 (numeric)], 1: [1 - col_2 (numeric)], 2: [2 - col_3 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 120.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | rabe_266 | [
"col_1",
"col_2"
] | [
false,
false
] | 657 |
211,927 | predictive_accuracy | accuracy_score | fourclass_scale | **Author**: Tin Kam Ho and Eugene M. Kleinberg.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**:
#Dataset from the LIBSVM data repository.
Preprocessing: transform to two-class | {0: [0 - att_1 (numeric)], 1: [1 - att_2 (numeric)], 2: [2 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 862.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | fourclass_scale | [
"att_1",
"att_2"
] | [
false,
false
] | 658 |
211,925 | predictive_accuracy | accuracy_score | fourclass | **Author**: Tin Kam Ho and Eugene M. Kleinberg.
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: Tin Kam Ho and Eugene M. Kleinberg.
Building projectable classifiers of arbitrary complexity.
In Proceedings of the 13th International Conference... | {0: [0 - att_1 (numeric)], 1: [1 - att_2 (numeric)], 2: [2 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 862.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | fourclass | [
"att_1",
"att_2"
] | [
false,
false
] | 659 |
5,288 | mean_absolute_error | mean_absolute_error | witmer_census_1980 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
A shar archive of data from the book Data Analysis: An
Introduction(1992) Prentice Hall bu Jeff Witmer. Submitted by
Jeff Witmer (fwitmer@ocvaxa.cc.oberlin.edu) [28/Jun/94] (29
kbytes)
Note: description taken from this web site:
http://lib.stat.c... | {0: [0 - STATE (nominal)],
1: [1 - OVER65Perc (numeric)],
2: [2 - MEDAGE (numeric)],
3: [3 - PERCAP$ (numeric)],
4: [4 - COLLEGEPerc (numeric)],
5: [5 - HSPerc (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | witmer_census_1980 | [
"OVER65Perc",
"MEDAGE",
"PERCAP$",
"COLLEGEPerc"
] | [
false,
false,
false,
false
] | 660 |
5,280 | mean_absolute_error | mean_absolute_error | fri_c3_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c3_250_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 661 |
211,884 | predictive_accuracy | accuracy_score | oh15.wc | null | {0: [0 - cluster (numeric)],
1: [1 - infus (numeric)],
2: [2 - gland (numeric)],
3: [3 - dopamin (numeric)],
4: [4 - phagocytosi (numeric)],
5: [5 - fetal (numeric)],
6: [6 - signific (numeric)],
7: [7 - penetr (numeric)],
8: [8 - hepat (numeric)],
9: [9 - cigarett (numeric)],
10: [10 - fusion (numeric)],
11... | {'MajorityClassSize': 157.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3101.0,
'NumberOfInstances': 913.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3100.0,
'NumberOfSymbolicFeatures': 1... | oh15.wc | [
"cluster",
"infus",
"gland",
"dopamin",
"phagocytosi",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fusion",
"nitroprussid",
"rifampin",
"resist",
"huvec",
"rest",
"quadricep",
"goal",
"hydroxi",
"nucleotid",
"echocardiographi",
"agent",
"0",
"placem",
"juli",... | [
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f... | 662 |
211,928 | predictive_accuracy | accuracy_score | german.numer | null | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 25.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | german.numer | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13",
"att_14",
"att_15",
"att_16",
"att_17",
"att_18",
"att_19",
"att_20",
"att_21",
"att_22",
"att_23",
"att_24"
] | [
false,
false,
false,
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false,
false,
false,
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 663 |
211,930 | predictive_accuracy | accuracy_score | heart | **Author**: Laboratory of Artificial Intelligence and Computer Science of the University of Porto (LIACC)
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**:
#Dataset from the LIBSVM data repository.
Preprocessing: scal... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 270.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | heart | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 664 |
5,277 | mean_absolute_error | mean_absolute_error | fri_c2_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c2_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
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false,
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false,
false,
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false,
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false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 665 |
211,926 | predictive_accuracy | accuracy_score | german.numer | null | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 25.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | german.numer | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13",
"att_14",
"att_15",
"att_16",
"att_17",
"att_18",
"att_19",
"att_20",
"att_21",
"att_22",
"att_23",
"att_24"
] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 666 |
211,897 | predictive_accuracy | accuracy_score | oh10.wc | null | {0: [0 - sudden (numeric)],
1: [1 - gland (numeric)],
2: [2 - signific (numeric)],
3: [3 - penetr (numeric)],
4: [4 - hepat (numeric)],
5: [5 - fusion (numeric)],
6: [6 - agenc (numeric)],
7: [7 - rest (numeric)],
8: [8 - seropreval (numeric)],
9: [9 - nucleotid (numeric)],
10: [10 - echocardiographi (numeric... | {'MajorityClassSize': 165.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 52.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3239.0,
'NumberOfInstances': 1050.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3238.0,
'NumberOfSymbolicFeatures': ... | oh10.wc | [
"sudden",
"gland",
"signific",
"penetr",
"hepat",
"fusion",
"agenc",
"rest",
"seropreval",
"nucleotid",
"echocardiographi",
"decision",
"agent",
"0",
"placem",
"environ",
"obstetr",
"vagin",
"overview",
"cytokin",
"reconstitut",
"discharg",
"f",
"ileal",
"g",
"clind... | [
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false,
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f... | 667 |
5,283 | mean_absolute_error | mean_absolute_error | sleuth_case1102 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - brain (numeric)],
1: [1 - liver (numeric)],
2: [2 - time (numeric)],
3: [3 - treat (nominal)],
4: [4 - days (nominal)],
5: [5 - sex (nominal)],
6: [6 - weight (numeric)],
7: [7 - loss (numeric)],
8: [8 - tumor (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 34.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | sleuth_case1102 | [
"brain",
"liver",
"time",
"treat",
"days",
"sex",
"weight",
"loss"
] | [
false,
false,
false,
true,
true,
true,
false,
false
] | 668 |
211,888 | predictive_accuracy | accuracy_score | oh0.wc | null | {0: [0 - depart (numeric)],
1: [1 - cluster (numeric)],
2: [2 - nephropathi (numeric)],
3: [3 - sudden (numeric)],
4: [4 - infus (numeric)],
5: [5 - gland (numeric)],
6: [6 - dopamin (numeric)],
7: [7 - fetal (numeric)],
8: [8 - signific (numeric)],
9: [9 - penetr (numeric)],
10: [10 - hepat (numeric)],
11: ... | {'MajorityClassSize': 194.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 51.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3183.0,
'NumberOfInstances': 1003.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3182.0,
'NumberOfSymbolicFeatures': ... | oh0.wc | [
"depart",
"cluster",
"nephropathi",
"sudden",
"infus",
"gland",
"dopamin",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fairli",
"resist",
"agenc",
"rest",
"seropreval",
"goal",
"nucleotid",
"hydroxi",
"echocardiographi",
"decision",
"agent",
"0",
"tongue",
... | [
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f... | 669 |
211,923 | predictive_accuracy | accuracy_score | svmguide1 | **Author**: Chih-Wei Hsu","Chih-Chung Chang","and Chih-Jen Lin.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin.
A practical guide to support vector classification.
Techni... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 7089.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | svmguide1 | [
"att_1",
"att_2",
"att_3",
"att_4"
] | [
false,
false,
false,
false
] | 670 |
5,275 | mean_absolute_error | mean_absolute_error | fri_c0_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c0_250_25 | [
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"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
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] | 671 |
211,943 | predictive_accuracy | accuracy_score | svmguide3 | **Author**: Chih-Wei Hsu","Chih-Chung Chang","and Chih-Jen Lin.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**:
#Dataset from the LIBSVM data repository.
Preprocessing: Original data: someone from Germany working wi... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
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'NumberOfNumericFeatures': 23.0,
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'co... | svmguide3 | [
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] | 672 |
211,891 | predictive_accuracy | accuracy_score | re1.wc | null | {0: [0 - imbal (numeric)],
1: [1 - subsidis (numeric)],
2: [2 - signific (numeric)],
3: [3 - fairli (numeric)],
4: [4 - rest (numeric)],
5: [5 - agenc (numeric)],
6: [6 - violent (numeric)],
7: [7 - francais (numeric)],
8: [8 - chuck (numeric)],
9: [9 - decision (numeric)],
10: [10 - agent (numeric)],
11: [1... | {'MajorityClassSize': 371.0,
'MaxNominalAttDistinctValues': 25.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 25.0,
'NumberOfFeatures': 3759.0,
'NumberOfInstances': 1657.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3758.0,
'NumberOfSymbolicFeatures': ... | re1.wc | [
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"subsidis",
"signific",
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"rest",
"agenc",
"violent",
"francais",
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"decision",
"agent",
"0",
"tower",
"margarin",
"placem",
"curtail",
"placer",
"environ",
"hamburg",
"und",
"fly",
"panamanian",
"housew",
"pension",
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"e",
"drake... | [
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f... | 673 |
5,281 | mean_absolute_error | mean_absolute_error | sleuth_ex1714 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - zip (numeric)],
1: [1 - fire (numeric)],
2: [2 - theft (numeric)],
3: [3 - age (numeric)],
4: [4 - income (numeric)],
5: [5 - race (numeric)],
6: [6 - vol (numeric)],
7: [7 - invol (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | sleuth_ex1714 | [
"zip",
"fire",
"theft",
"age",
"race",
"vol",
"invol"
] | [
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] | 674 |
211,890 | predictive_accuracy | accuracy_score | oh5.wc | **Author**: [George Forman](https://scholar.google.com/citations?user=r0a222QAAAAJ)
**Source**: http://tunedit.org/repo/Data/Text-wc/oh5.wc.arff
**Please cite**: | {0: [0 - depart (numeric)],
1: [1 - nephropathi (numeric)],
2: [2 - cluster (numeric)],
3: [3 - tenth (numeric)],
4: [4 - sudden (numeric)],
5: [5 - infus (numeric)],
6: [6 - imbal (numeric)],
7: [7 - gland (numeric)],
8: [8 - cyclophosphamid (numeric)],
9: [9 - furth (numeric)],
10: [10 - phagocytosi (numeri... | {'MajorityClassSize': 149.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 59.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3013.0,
'NumberOfInstances': 918.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3012.0,
'NumberOfSymbolicFeatures': 1... | oh5.wc | [
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"nephropathi",
"cluster",
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"sudden",
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"imbal",
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"phagocytosi",
"fetal",
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"hepat",
"cigarett",
"fusion",
"resist",
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"echocardiograph... | [
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f... | 675 |
5,276 | mean_absolute_error | mean_absolute_error | fri_c0_500_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)]} | {'MajorityClassSize': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c0_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
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false,
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] | 676 |
4,796 | mean_absolute_error | mean_absolute_error | topo_2_1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode).
The molecules and outputs are taken from the original studies (see below). The other... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 267.0,
'NumberOfInstances': 8885.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 267.0,
'NumberOfSymbolicFeatures': 0.0,
'... | topo_2_1 | [
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f... | 677 |
5,272 | mean_absolute_error | mean_absolute_error | fri_c0_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
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'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c0_500_50 | [
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"oz28",
"oz29",
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"oz33... | [
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f... | 678 |
211,882 | predictive_accuracy | accuracy_score | tr21.wc | null | {0: [0 - noteworthi (numeric)],
1: [1 - parad (numeric)],
2: [2 - naval (numeric)],
3: [3 - ward (numeric)],
4: [4 - dock (numeric)],
5: [5 - logist (numeric)],
6: [6 - tonnag (numeric)],
7: [7 - maine (numeric)],
8: [8 - ap (numeric)],
9: [9 - sallie (numeric)],
10: [10 - franci (numeric)],
11: [11 - libert... | {'MajorityClassSize': 231.0,
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'MinorityClassSize': 4.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 7903.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7902.0,
'NumberOfSymbolicFeatures': 1.0,... | tr21.wc | [
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"parad",
"naval",
"ward",
"dock",
"logist",
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"maine",
"ap",
"sallie",
"franci",
"libertarian",
"pip",
"russell",
"sanford",
"approp",
"ind",
"shipboard",
"sunset",
"servant",
"depart",
"logansport",
"number",
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"maritim",
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... | [
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f... | 679 |
211,922 | predictive_accuracy | accuracy_score | colon-cancer | **Author**: U. Alon","N. Barkai","D. A. Notterman","K. Gish","S.Ybarra","D.Mack","and A. J. Levine.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: U. Alon, N. Barkai, D. A. Notterman, K. Gish, S.Ybarra, D.Mack, and A. J... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
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'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2001.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2001.0,
'NumberOfSymbolicFeatures': 0.0,
'... | colon-cancer | [
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f... | 680 |
5,290 | mean_absolute_error | mean_absolute_error | chscase_census5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | chscase_census5 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 681 |
146,869 | predictive_accuracy | accuracy_score | duke-breast-cancer | **Author**: Shirish Krishnaj Shevade and S. Sathiya Keerthi.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: Shirish Krishnaj Shevade and S. Sathiya Keerthi.
A simple and efficient algorithm for gene selection using spar... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7130.0,
'NumberOfInstances': 86.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7130.0,
'NumberOfSymbolicFeatures': 0.0,
'... | duke-breast-cancer | [
"att_1",
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f... | 682 |
5,289 | mean_absolute_error | mean_absolute_error | chscase_adopt | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 39.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 2.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | chscase_adopt | [
"col_2",
"col_3"
] | [
false,
false
] | 683 |
211,886 | predictive_accuracy | accuracy_score | tr23.wc | null | {0: [0 - utmost (numeric)],
1: [1 - secondli (numeric)],
2: [2 - neglect (numeric)],
3: [3 - shalala (numeric)],
4: [4 - weekend (numeric)],
5: [5 - timefram (numeric)],
6: [6 - formul (numeric)],
7: [7 - lump (numeric)],
8: [8 - belt (numeric)],
9: [9 - cranston (numeric)],
10: [10 - sympathi (numeric)],
11... | {'MajorityClassSize': 91.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 6.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 5833.0,
'NumberOfInstances': 204.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5832.0,
'NumberOfSymbolicFeatures': 1.0,
... | tr23.wc | [
"utmost",
"secondli",
"neglect",
"shalala",
"weekend",
"timefram",
"formul",
"lump",
"belt",
"cranston",
"sympathi",
"wrap",
"lectur",
"underfund",
"embargo",
"glad",
"glar",
"multifamili",
"perkin",
"messr",
"demean",
"cop",
"depart",
"multiyear",
"blue",
"romero",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 684 |
211,885 | predictive_accuracy | accuracy_score | tr11.wc | null | {0: [0 - outfit (numeric)],
1: [1 - hasn (numeric)],
2: [2 - calm (numeric)],
3: [3 - gene (numeric)],
4: [4 - resettl (numeric)],
5: [5 - lotteri (numeric)],
6: [6 - privileg (numeric)],
7: [7 - junior (numeric)],
8: [8 - withdrawn (numeric)],
9: [9 - chok (numeric)],
10: [10 - compaq (numeric)],
11: [11 - ... | {'MajorityClassSize': 132.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 6.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 6430.0,
'NumberOfInstances': 414.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6429.0,
'NumberOfSymbolicFeatures': 1.0,... | tr11.wc | [
"outfit",
"hasn",
"calm",
"gene",
"resettl",
"lotteri",
"privileg",
"junior",
"withdrawn",
"chok",
"compaq",
"noi",
"colombia",
"radioact",
"neutron",
"npc",
"disabl",
"macedonian",
"eman",
"skopje",
"volkov",
"appall",
"sidelin",
"titanium",
"quasi",
"league",
"n... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 685 |
211,881 | predictive_accuracy | accuracy_score | tr45.wc | null | {0: [0 - sunni (numeric)],
1: [1 - interbank (numeric)],
2: [2 - rig (numeric)],
3: [3 - dlouhi (numeric)],
4: [4 - bratislava (numeric)],
5: [5 - librari (numeric)],
6: [6 - number (numeric)],
7: [7 - contamin (numeric)],
8: [8 - mold (numeric)],
9: [9 - norm (numeric)],
10: [10 - mobil (numeric)],
11: [11 ... | {'MajorityClassSize': 160.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 14.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 8262.0,
'NumberOfInstances': 690.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8261.0,
'NumberOfSymbolicFeatures': 1... | tr45.wc | [
"sunni",
"interbank",
"rig",
"dlouhi",
"bratislava",
"librari",
"number",
"contamin",
"mold",
"norm",
"mobil",
"sens",
"plankton",
"ash",
"workstat",
"cabin",
"sunken",
"tengiz",
"cove",
"tragedi",
"alloi",
"fluid",
"escort",
"allot",
"curi",
"tac",
"komsomolet",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 686 |
5,279 | mean_absolute_error | mean_absolute_error | fri_c2_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting
The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber".
Friedman is the one of the most... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | fri_c2_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 687 |
5,282 | mean_absolute_error | mean_absolute_error | rabe_265 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This file contains data from Regression Analysis By Example, 2nd Edition,
by Samprit Chatterjee and Bertram Price, John Wiley, 1991.
Data sets have names of the form 'rabe.xxx' where xxx is the page number
in the book where the data occurs.
For a... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 51.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | rabe_265 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6"
] | [
false,
false,
false,
false,
false,
false
] | 688 |
5,285 | mean_absolute_error | mean_absolute_error | chscase_census6 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | chscase_census6 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6"
] | [
false,
false,
false,
false,
false,
false
] | 689 |
211,893 | predictive_accuracy | accuracy_score | tr12.wc | null | {0: [0 - saga (numeric)],
1: [1 - stumble (numeric)],
2: [2 - abu (numeric)],
3: [3 - membership (numeric)],
4: [4 - rc (numeric)],
5: [5 - cherish (numeric)],
6: [6 - airwai (numeric)],
7: [7 - concur (numeric)],
8: [8 - ravag (numeric)],
9: [9 - perestroyka (numeric)],
10: [10 - rf (numeric)],
11: [11 - 0 ... | {'MajorityClassSize': 93.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 5805.0,
'NumberOfInstances': 313.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5804.0,
'NumberOfSymbolicFeatures': 1.0,
... | tr12.wc | [
"saga",
"stumble",
"abu",
"membership",
"rc",
"cherish",
"airwai",
"concur",
"ravag",
"perestroyka",
"rf",
"0",
"environ",
"purchas",
"loss",
"lost",
"manag",
"particip",
"southeast",
"product",
"dinner",
"anglo",
"aqueou",
"deton",
"conspiraci",
"ethic",
"criteri... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 690 |
5,295 | mean_absolute_error | mean_absolute_error | visualizing_slope | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This S dump contains 22 data sets from the
book Visualizing Data published by
Hobart Press (books@hobart.com).
The dump was created by data.dump()
and can be read back into S by data.restore().
The name of each S data set is the name of
the data s... | {0: [0 - error (numeric)],
1: [1 - percent (numeric)],
2: [2 - distance (numeric)],
3: [3 - resolution (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 44.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | visualizing_slope | [
"error",
"percent",
"distance"
] | [
false,
false,
false
] | 691 |
5,291 | mean_absolute_error | mean_absolute_error | chscase_census4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | chscase_census4 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 692 |
5,287 | mean_absolute_error | mean_absolute_error | rmftsa_ladata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data Sets for 'Regression Models for Time Series Analysis' by
B. Kedem and K. Fokianos, Wiley 2002. Submitted by Kostas
Fokianos (fokianos@ucy.ac.cy) [8/Nov/02] (176k)
Note: - attribute names were generated manually
- information about data taken... | {0: [0 - Total_Mortality (numeric)],
1: [1 - Respiratory_Mortality (numeric)],
2: [2 - Cardiovascular_Mortality (numeric)],
3: [3 - Temperature (numeric)],
4: [4 - Relative_Humidity (numeric)],
5: [5 - Carbon_Monoxide (numeric)],
6: [6 - Sulfur_Dioxideglm.LAshumway (numeric)],
7: [7 - Nitrogen_Dioxide (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 508.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | rmftsa_ladata | [
"Total_Mortality",
"Cardiovascular_Mortality",
"Temperature",
"Relative_Humidity",
"Carbon_Monoxide",
"Sulfur_Dioxideglm.LAshumway",
"Nitrogen_Dioxide",
"Hydrocarbons",
"Ozone",
"Particulates"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 693 |
5,294 | mean_absolute_error | mean_absolute_error | chscase_demand | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_5 (numeric)],
4: [4 - col_6 (numeric)],
5: [5 - col_7 (numeric)],
6: [6 - col_8 (numeric)],
7: [7 - col_9 (numeric)],
8: [8 - col_10 (numeric)],
9: [9 - col_11 (numeric)],
10: [10 - col_12 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 27.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | chscase_demand | [
"col_1",
"col_2",
"col_3",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 694 |
211,883 | predictive_accuracy | accuracy_score | tr31.wc | null | {0: [0 - cone (numeric)],
1: [1 - protection (numeric)],
2: [2 - wir (numeric)],
3: [3 - fright (numeric)],
4: [4 - resold (numeric)],
5: [5 - thirsti (numeric)],
6: [6 - isolina (numeric)],
7: [7 - lucho (numeric)],
8: [8 - wari (numeric)],
9: [9 - sermon (numeric)],
10: [10 - unwis (numeric)],
11: [11 - co... | {'MajorityClassSize': 352.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 10129.0,
'NumberOfInstances': 927.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10128.0,
'NumberOfSymbolicFeatures': 1.... | tr31.wc | [
"cone",
"protection",
"wir",
"fright",
"resold",
"thirsti",
"isolina",
"lucho",
"wari",
"sermon",
"unwis",
"commenc",
"optic",
"ah",
"reservoir",
"au",
"uninjur",
"deflat",
"enclav",
"narcodollar",
"delgado",
"jakarta",
"oxapampa",
"yonsei",
"modul",
"labell",
"in... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 695 |
5,286 | mean_absolute_error | mean_absolute_error | sleuth_case2002 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - FM (nominal)],
1: [1 - LC (nominal)],
2: [2 - BK (nominal)],
3: [3 - SS (nominal)],
4: [4 - AG (numeric)],
5: [5 - YR (numeric)],
6: [6 - CD (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 147.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 4.0,
'cost_... | sleuth_case2002 | [
"FM",
"LC",
"BK",
"SS",
"AG",
"YR"
] | [
true,
true,
true,
true,
false,
false
] | 696 |
5,305 | mean_absolute_error | mean_absolute_error | chscase_vine1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)],
9: [9 - col_10 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | chscase_vine1 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 697 |
5,301 | mean_absolute_error | mean_absolute_error | sleuth_ex1605 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - FMED (numeric)],
1: [1 - TMIQ (numeric)],
2: [2 - Age2IQ (numeric)],
3: [3 - Age4IQ (numeric)],
4: [4 - Age8IQ (numeric)],
5: [5 - Age13IQ (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | sleuth_ex1605 | [
"FMED",
"TMIQ",
"Age2IQ",
"Age4IQ",
"Age8IQ"
] | [
false,
false,
false,
false,
false
] | 698 |
211,934 | predictive_accuracy | accuracy_score | splice | **Author**: Delve Datasets
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**:
#Dataset from the LIBSVM data repository.
Preprocessing: scaled to [-1,1] | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 3175.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 61.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | splice | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13",
"att_14",
"att_15",
"att_16",
"att_17",
"att_18",
"att_19",
"att_20",
"att_21",
"att_22",
"att_23",
"att_24",
"att_25",
"att_26",
"att_27",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 699 |
5,292 | mean_absolute_error | mean_absolute_error | chscase_census3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | chscase_census3 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 700 |
5,308 | mean_absolute_error | mean_absolute_error | chatfield_4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This file is a text file giving details about the time series analysed
in 'The Analysis of Time Series' by Chris Chatfield.
The 5th edn was published in 1996 and the 6th edn in 2003.
The series are listed separately at the author's home web site a... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)],
9: [9 - col_10 (numeric)],
10: [10 - col_11 (numeric)],
11: [11 - col_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 235.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | chatfield_4 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 701 |
211,896 | predictive_accuracy | accuracy_score | tr41.wc | null | {0: [0 - lighthous (numeric)],
1: [1 - conquest (numeric)],
2: [2 - algerian (numeric)],
3: [3 - banish (numeric)],
4: [4 - jerri (numeric)],
5: [5 - nationalist (numeric)],
6: [6 - evil (numeric)],
7: [7 - beirut (numeric)],
8: [8 - worrisom (numeric)],
9: [9 - stolen (numeric)],
10: [10 - durabl (numeric)],... | {'MajorityClassSize': 243.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 7455.0,
'NumberOfInstances': 878.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7454.0,
'NumberOfSymbolicFeatures': 1.... | tr41.wc | [
"lighthous",
"conquest",
"algerian",
"banish",
"jerri",
"nationalist",
"evil",
"beirut",
"worrisom",
"stolen",
"durabl",
"adburgham",
"restart",
"lee",
"avonmouth",
"wive",
"exot",
"tighter",
"tenth",
"polic",
"diane",
"perish",
"altimet",
"princeton",
"eb",
"diseas... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 702 |
5,311 | mean_absolute_error | mean_absolute_error | rabe_176 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This file contains data from Regression Analysis By Example, 2nd Edition,
by Samprit Chatterjee and Bertram Price, John Wiley, 1991.
Data sets have names of the form 'rabe.xxx' where xxx is the page number
in the book where the data occurs.
For a... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 70.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | rabe_176 | [
"col_2",
"col_3",
"col_4"
] | [
false,
false,
false
] | 703 |
5,303 | mean_absolute_error | mean_absolute_error | chscase_vine2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)], 1: [1 - col_2 (numeric)], 2: [2 - col_3 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 468.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | chscase_vine2 | [
"col_1",
"col_2"
] | [
false,
false
] | 704 |
212,053 | predictive_accuracy | accuracy_score | fourclass | **Author**: Tin Kam Ho and Eugene M. Kleinberg.
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: Tin Kam Ho and Eugene M. Kleinberg.
Building projectable classifiers of arbitrary complexity.
In Proceedings of the 13th International Conference... | {0: [0 - att_1 (numeric)], 1: [1 - att_2 (numeric)], 2: [2 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 862.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | fourclass | [
"att_1",
"att_2"
] | [
false,
false
] | 705 |
5,302 | mean_absolute_error | mean_absolute_error | visualizing_soil | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This S dump contains 22 data sets from the
book Visualizing Data published by
Hobart Press (books@hobart.com).
The dump was created by data.dump()
and can be read back into S by data.restore().
The name of each S data set is the name of
the data s... | {0: [0 - northing (numeric)],
1: [1 - easting (numeric)],
2: [2 - resistivity (numeric)],
3: [3 - isns (nominal)],
4: [4 - track (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 8641.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_soil | [
"northing",
"easting",
"resistivity",
"isns"
] | [
false,
false,
false,
true
] | 706 |
212,016 | predictive_accuracy | accuracy_score | oh0.wc | null | {0: [0 - depart (numeric)],
1: [1 - cluster (numeric)],
2: [2 - nephropathi (numeric)],
3: [3 - sudden (numeric)],
4: [4 - infus (numeric)],
5: [5 - gland (numeric)],
6: [6 - dopamin (numeric)],
7: [7 - fetal (numeric)],
8: [8 - signific (numeric)],
9: [9 - penetr (numeric)],
10: [10 - hepat (numeric)],
11: ... | {'MajorityClassSize': 194.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 51.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3183.0,
'NumberOfInstances': 1003.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3182.0,
'NumberOfSymbolicFeatures': ... | oh0.wc | [
"depart",
"cluster",
"nephropathi",
"sudden",
"infus",
"gland",
"dopamin",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fairli",
"resist",
"agenc",
"rest",
"seropreval",
"goal",
"nucleotid",
"hydroxi",
"echocardiographi",
"decision",
"agent",
"0",
"tongue",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 708 |
212,012 | predictive_accuracy | accuracy_score | oh15.wc | null | {0: [0 - cluster (numeric)],
1: [1 - infus (numeric)],
2: [2 - gland (numeric)],
3: [3 - dopamin (numeric)],
4: [4 - phagocytosi (numeric)],
5: [5 - fetal (numeric)],
6: [6 - signific (numeric)],
7: [7 - penetr (numeric)],
8: [8 - hepat (numeric)],
9: [9 - cigarett (numeric)],
10: [10 - fusion (numeric)],
11... | {'MajorityClassSize': 157.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3101.0,
'NumberOfInstances': 913.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3100.0,
'NumberOfSymbolicFeatures': 1... | oh15.wc | [
"cluster",
"infus",
"gland",
"dopamin",
"phagocytosi",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fusion",
"nitroprussid",
"rifampin",
"resist",
"huvec",
"rest",
"quadricep",
"goal",
"hydroxi",
"nucleotid",
"echocardiographi",
"agent",
"0",
"placem",
"juli",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 709 |
5,310 | mean_absolute_error | mean_absolute_error | rabe_97 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This file contains data from Regression Analysis By Example, 2nd Edition,
by Samprit Chatterjee and Bertram Price, John Wiley, 1991.
Data sets have names of the form 'rabe.xxx' where xxx is the page number
in the book where the data occurs.
For a... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (nominal)],
4: [4 - col_5 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 46.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_m... | rabe_97 | [
"col_2",
"col_3",
"col_4"
] | [
false,
false,
true
] | 710 |
212,055 | predictive_accuracy | accuracy_score | fourclass_scale | **Author**: Tin Kam Ho and Eugene M. Kleinberg.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**:
#Dataset from the LIBSVM data repository.
Preprocessing: transform to two-class | {0: [0 - att_1 (numeric)], 1: [1 - att_2 (numeric)], 2: [2 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 862.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | fourclass_scale | [
"att_1",
"att_2"
] | [
false,
false
] | 711 |
212,021 | predictive_accuracy | accuracy_score | tr12.wc | null | {0: [0 - saga (numeric)],
1: [1 - stumble (numeric)],
2: [2 - abu (numeric)],
3: [3 - membership (numeric)],
4: [4 - rc (numeric)],
5: [5 - cherish (numeric)],
6: [6 - airwai (numeric)],
7: [7 - concur (numeric)],
8: [8 - ravag (numeric)],
9: [9 - perestroyka (numeric)],
10: [10 - rf (numeric)],
11: [11 - 0 ... | {'MajorityClassSize': 93.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 5805.0,
'NumberOfInstances': 313.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5804.0,
'NumberOfSymbolicFeatures': 1.0,
... | tr12.wc | [
"saga",
"stumble",
"abu",
"membership",
"rc",
"cherish",
"airwai",
"concur",
"ravag",
"perestroyka",
"rf",
"0",
"environ",
"purchas",
"loss",
"lost",
"manag",
"particip",
"southeast",
"product",
"dinner",
"anglo",
"aqueou",
"deton",
"conspiraci",
"ethic",
"criteri... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 712 |
5,313 | mean_absolute_error | mean_absolute_error | chscase_whale | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
chscase A collection of the data sets used in the book
"A Casebook for a First Course in Statistics and Data Analysis,"
by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff,
John Wiley and Sons, New York, 1995.... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_6 (numeric)],
5: [5 - col_7 (numeric)],
6: [6 - col_8 (numeric)],
7: [7 - col_9 (numeric)],
8: [8 - col_10 (numeric)],
9: [9 - col_12 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 228.0,
'NumberOfInstancesWithMissingValues': 5.0,
'NumberOfMissingValues': 25.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | chscase_whale | [
"col_2",
"col_3",
"col_4",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 713 |
212,058 | predictive_accuracy | accuracy_score | heart | **Author**: Laboratory of Artificial Intelligence and Computer Science of the University of Porto (LIACC)
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**:
#Dataset from the LIBSVM data repository.
Preprocessing: scal... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 270.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | heart | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 714 |
212,056 | predictive_accuracy | accuracy_score | german.numer | null | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 25.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | german.numer | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13",
"att_14",
"att_15",
"att_16",
"att_17",
"att_18",
"att_19",
"att_20",
"att_21",
"att_22",
"att_23",
"att_24"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 715 |
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