Data
tokyo1

tokyo1

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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  • Chemistry Machine Learning study_144
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Author: Ron Kohavi Source: [SGI.com tech archive](http://www.sgi.com/tech/mlc/db/) (no longer available, [copy on GitHub](https://github.com/acefoxy/DataScience/blob/973d9239ca3190487204ce8037a1d3c8689f95dd/week2/www.sgi.com/tech/mlc/db/tokyo1.names )), [PMLB](https://github.com/EpistasisLab/penn-ml-benchmarks/tree/master/datasets/classification/tokyo1) Please cite: Tokyo SGI Server Performance Data This is Performance co-pilot (PCP) data for the Tokyo server at Silicon Graphics International (SGI). It characterizes the server performance as either `good` (1) or `bad` (0). The instances are measurements generated by the PCP software every five seconds. See the [PCP manual](http://www.irix7.com/techpubs/007-2614-001.pdf) for further details. ### Attribute Information The attributes are interpreted as follows: - runq: Average number of runnable processes in main memory (mem) and in swap memory (swp) during the interval. - memory: The free column indicates average free memory during the interval, in Kilobytes. The page column is the average number of page out operations per second during the interval. I/O operations caused by these page-out operations are included in the write I/O rate. - system: System call rate (scall), context switch rate (ctxsw) and interrupt rate (intr). Rates are expressed as average operations per second during the interval. - disks: Aggregated physical read (rd) and write (wr) rates over all disks, expressed as physical I/O operations issued per second during the interval. These rates are independent of the I/O block size. - cpu: Percentage of CPU time spent executing user code (usr), system and interrupt code (sys), idle loop (idl) and idle waiting for resources, typically disk I/O (wt).

45 features

class (target)nominal2 unique values
0 missing
cpu_avg_usernumeric813 unique values
0 missing
cpu_avg_sysnumeric904 unique values
0 missing
cpu_avg_busynumeric901 unique values
0 missing
cpu_avg_waitnumeric868 unique values
0 missing
cpu_avg_idlenumeric895 unique values
0 missing
cpu_avg_wastenumeric811 unique values
0 missing
cpu_max_usernumeric624 unique values
0 missing
cpu_max_sysnumeric870 unique values
0 missing
cpu_max_busynumeric897 unique values
0 missing
cpu_max_waitnumeric843 unique values
0 missing
cpu_max_idlenumeric867 unique values
0 missing
cpu_max_wastenumeric636 unique values
0 missing
cpu_frac_busynumeric9 unique values
0 missing
io_igetnumeric840 unique values
0 missing
io_breadnumeric885 unique values
0 missing
io_bwritenumeric914 unique values
0 missing
io_lreadnumeric868 unique values
0 missing
io_lwritenumeric2 unique values
0 missing
io_phreadnumeric293 unique values
0 missing
io_phwritenumeric279 unique values
0 missing
io_wcancelnumeric834 unique values
0 missing
io_nameinominal1 unique values
0 missing
io_dirblknumeric844 unique values
0 missing
disk_avg_activenumeric854 unique values
0 missing
disk_max_activenumeric4 unique values
0 missing
disk_frac_activenumeric770 unique values
0 missing
disk_avg_readnumeric755 unique values
0 missing
disk_avg_writenumeric807 unique values
0 missing
disk_avg_totalnumeric656 unique values
0 missing
disk_max_readnumeric663 unique values
0 missing
disk_max_writenumeric745 unique values
0 missing
disk_max_totalnumeric3 unique values
0 missing
disk_frac_busynumeric819 unique values
0 missing
net_avg_readnumeric882 unique values
0 missing
net_avg_writenumeric829 unique values
0 missing
net_avg_totalnumeric830 unique values
0 missing
net_max_readnumeric819 unique values
0 missing
net_max_writenumeric844 unique values
0 missing
net_max_totalnumeric4 unique values
0 missing
net_frac_busynominal1 unique values
0 missing
mem_swapnumeric483 unique values
0 missing
mem_faultnumeric30 unique values
0 missing
mem_tlbflushnumeric896 unique values
0 missing
syscall_totalnumeric248 unique values
0 missing
Show all 45 features

62 properties

NumberOfInstances
959
Number of instances (rows) of the dataset.
NumberOfFeatures
45
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
42
Number of numeric attributes.
NumberOfSymbolicFeatures
3
Number of nominal attributes.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
5.01
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
38.24
Second quartile (Median) of means among attributes of the numeric type.
MajorityClassPercentage
63.92
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
154257.65
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
613
Number of instances belonging to the most frequent class.
MinAttributeEntropy
0
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
2.51
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
0
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-0.69
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
2.22
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
64.18
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
959
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.01
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
0
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
2584689.78
Maximum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
82.4
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
0
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
93.33
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
517.53
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.29
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
6.67
Percentage of nominal attributes.
Quartile3MutualInformation
0
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
30.97
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.05
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
0
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
8.74
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
3352056.49
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
36.08
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
0.13
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
2157.85
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0
Average entropy of the attributes.
MinorityClassSize
346
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
0.47
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0.58
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
64.16
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
132026.11
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
0.87
First quartile of skewness among attributes of the numeric type.
AutoCorrelation
0.53
Average class difference between consecutive instances.
MeanMutualInformation
0
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
1.58
First quartile of standard deviation of attributes of the numeric type.
ClassEntropy
0.94
Entropy of the target attribute values.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
0
Second quartile (Median) of entropy among attributes.
Dimensionality
0.05
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
1.33
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
6.45
Second quartile (Median) of kurtosis among attributes of the numeric type.
Show all 62 properties

23 tasks

Supervised Classification on tokyo1
37 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on tokyo1
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on tokyo1
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on tokyo1
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on tokyo1
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
Clustering on tokyo1
0 runs - estimation_procedure: 50 times Clustering
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