Data
Intersectional-Bias-Assessment

Intersectional-Bias-Assessment

active ARFF CC-4Y Visibility: public Uploaded 11-01-2023 by Manojit Nandi
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This synthetic dataset contains demographic and clinical data used to train a classifier to predict a diagnosis (of schizophrenia or depression) and assess the model performance for intersectional bias. This dataset is used in the tutorial 'An Intersectional Approach to Model Construction and Evaluation in Mental Health Care' presented at ACM FAccT 2022.

20 features

Diagnosis (target)numeric2 unique values
0 missing
datasetstring2 unique values
0 missing
Sexstring2 unique values
0 missing
Racestring4 unique values
0 missing
Housingstring2 unique values
0 missing
Delaystring2 unique values
0 missing
Anhedonianumeric11000 unique values
0 missing
Dep_Moodnumeric11000 unique values
0 missing
Sleepnumeric11000 unique values
0 missing
Tirednumeric11000 unique values
0 missing
Appetitenumeric11000 unique values
0 missing
Ruminationnumeric11000 unique values
0 missing
Concentrationnumeric11000 unique values
0 missing
Psychomotornumeric11000 unique values
0 missing
Delusionnumeric11000 unique values
0 missing
Suspiciousnumeric11000 unique values
0 missing
Withdrawalnumeric11000 unique values
0 missing
Passivenumeric11000 unique values
0 missing
Tensionnumeric11000 unique values
0 missing
Unusual_Thoughtnumeric10999 unique values
0 missing
Show all 20 features

19 properties

NumberOfInstances
11000
Number of instances (rows) of the dataset.
NumberOfFeatures
20
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
15
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
AutoCorrelation
0.5
Average class difference between consecutive instances.
PercentageOfMissingValues
0
Percentage of missing values.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
75
Percentage of numeric attributes.
Show all 19 properties

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