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Graph_Inference_Dataset

Graph_Inference_Dataset

active ARFF Publicly available Visibility: public Uploaded 16-03-2022 by Robin Wise
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//Add the description.md of the data file Graph_Inference_Dataset Goudet, Olivier, 2017, "Graph inference datasets. Replication Data for: "Learning Functional Causal Models with Generative Neural Networks"", [Link](https://doi.org/10.7910/DVN/UZMB69), Harvard Dataverse, V1, UNF:6:wrgpGhxTNPqE4R5S2cNcpg== [fileUNF] Graph datasets in csv format. Used in the article Learning Functional Causal Models with Generative Neural Networks. 1) File *_numdata.csv contain the data of around 20 variables connected in a graph without hidden variables. G2, G3, G4 and G5 refered to graph with 2, 3, 4 and 5 parents maximum for each node. Each file *_target.csv contains the ground truth of the graph with cause -> effect File beginning by "Big" are larger graphs with 100 variables. 2) Each file *_confounders_numdata.csv contain the data of around 20 variables connected in a graph. There are 3 hidden variables. Each file *_confounders_skeleton.csv contains the skeleton of the graph (including spurious links due to common hidden cause). Each file *_confounders_target.csv contains the ground truth of the graph with the direct visible cause -> effect. The task is to recover the direct visible links cause->effect while removing the spurious links of the skeleton (2017-08-24)

101 features

V0numeric500 unique values
0 missing
V1numeric500 unique values
0 missing
V2numeric500 unique values
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V3numeric500 unique values
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Show first 100 features

19 properties

NumberOfInstances
500
Number of instances (rows) of the dataset.
NumberOfFeatures
101
Number of attributes (columns) of the dataset.
NumberOfClasses
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
101
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
AutoCorrelation
Average class difference between consecutive instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
NumberOfBinaryFeatures
0
Number of binary attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
Dimensionality
0.2
Number of attributes divided by the number of instances.
Show all 19 properties

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