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stock_fardamento02

stock_fardamento02

active ARFF Public Domain (CC0) Visibility: public Uploaded 22-06-2020 by Rui Henriques
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Author: Source: Unknown - Date unknown Please cite: valores de saida de fardamento com temperaturas, admissões e demissões

7 features

qts (target)numeric113 unique values
0 missing
Materialnumeric261 unique values
0 missing
Dianominal183 unique values
0 missing
ppnumeric32 unique values
0 missing
TEMPnumeric91 unique values
0 missing
admnumeric16 unique values
0 missing
Demnumeric16 unique values
0 missing
Show all 7 features

19 properties

NumberOfInstances
6277
Number of instances (rows) of the dataset.
NumberOfFeatures
7
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
6
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
85.71
Percentage of numeric attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
14.29
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
-2.01
Average class difference between consecutive instances.
PercentageOfMissingValues
0
Percentage of missing values.
Show all 19 properties

9 tasks

Supervised Regression on stock_fardamento02
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: qts
Supervised Regression on stock_fardamento02
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: qts
Supervised Regression on stock_fardamento02
0 runs - estimation_procedure: 33% Holdout set - target_feature: qts
Clustering on stock_fardamento02
0 runs - estimation_procedure: 50 times Clustering
Clustering on stock_fardamento02
0 runs - estimation_procedure: 50 times Clustering
Clustering on stock_fardamento02
0 runs - estimation_procedure: 50 times Clustering
Clustering on stock_fardamento02
0 runs - estimation_procedure: 50 times Clustering
Clustering on stock_fardamento02
0 runs - estimation_procedure: 50 times Clustering
Clustering on stock_fardamento02
0 runs - estimation_procedure: 50 times Clustering
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