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humans_numeric

humans_numeric

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Title: Assessing the Reliability of a Human Estimator %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable, verifiable, refutable, and/or improvable predictive models of software engineering. If you publish material based on PROMISE data sets then, please follow the acknowledgment guidelines posted on the PROMISE repository web page http://promisedata.org/repository . %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (c) 2007 : Gary Boetticher : boetticher AT uhcl DOT edu Phone: +1 (281) 283 8305 This data set is distributed under the Creative Commons Attribution-Share Alike 3.0 License http://creativecommons.org/licenses/by-sa/3.0/ You are free: * to Share -- copy, distribute and transmit the work * to Remix -- to adapt the work Under the following conditions: Attribution. You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under the same, similar or a compatible license. * For any reuse or distribution, you must make clear to others the license terms of this work. * Any of the above conditions can be waived if you get permission from the copyright holder. * Apart from the remix rights granted under this license, nothing in this license impairs or restricts the author's moral rights. 2. Sources (a) Creator: Gary D. Boetticher (b) Date: February 20, 2007 (c) Contact: boetticher AT uhcl DOT edu Phone: +1 (281) 283 8305 3. Donor: Gary D. Boetticher 4. Past Usage: This data was used for: Boetticher, G., Lokhandwala, N., James C. Helm, Understanding the Human Estimator, Second International Predictive Models in Software Engineering (PROMISE) Workshop co-located at the 22nd IEEE International Conference on Software Maintenance, Philadelphia, PA, September, 2006. More information is available at http://nas.cl.uh.edu/boetticher/research.html Since PROMISE 2006, the data set expanded by about 50 percent. The additional tuples allowed us to divide the data into 3 major categories. Those who severely underestimate (first 25 tuples). Those who accurately estimate (next 25 tuples). And those who severely overestimate (last 25 tuples). The PROMISE 2007 experiments compare the underestimators with the accurate estimators and the overestimators with the accurate estimators. 5. Number of Instances: 75 6. Number of Attributes: 14 independent variables and 1 dependent variable 7. Attribute Information: Numeric Degree: This attribute refers to the level of education of the participant. 2=High School, 3=Bachelors, 4=Masters,5=Ph.D. TechUGCourses: This refers to the number of technical undergraduate courses that the participant has taken. TechGCourses: This refers to the number of technical graduate courses that the participant has taken. MgmtUGCourses: This refers to the number of management undergraduate courses that the participant has taken. MgmtGCourses: This refers to the number of management graduate courses that the participant has taken. Total Workshops: This refers to the total number of workshops that the participant has attended. Total Conferences: This refers to the total number of conferences that the participant has attended. TotalLangExp: This refers to the total number of languages and experience in those languages that the participant has. Hardware Proj Mgmt Exp: This corresponds to the total amount of time that the respondant has been estimating hardware projects. Software Proj Mgmt Exp: This corresponds to the total amount of time that the respondant has been estimating software projects. No Of Hardware Proj Estimated: This refers to the total number of hardware projects that the participant has estimated. No Of Software Proj Estimated: This refers to the total number of software projects that the participant has estimated. Domain Exp: The domain experience refers to how much experience the participant has in the oil and gas industry. Procurement Industry Exp: The procurement industry experience refers to the amount of time, in years, that the participant has regarding procurement. ABS((TotalEstimates-TotalActual)/TotalActual): This is the class variable. It represents the overall relative error for the participant's estimates.

15 features

ABS((TotalEstimates-TotalActual)/TotalActual) (target)numeric72 unique values
0 missing
Numeric Degreenumeric5 unique values
0 missing
TechUGCoursesnumeric31 unique values
0 missing
TechGCoursesnumeric18 unique values
0 missing
MgmtUGCoursesnumeric9 unique values
0 missing
MgmtGCoursesnumeric9 unique values
0 missing
Total Workshopsnumeric17 unique values
0 missing
Total Conferencesnumeric14 unique values
0 missing
TotalLangExpnumeric45 unique values
0 missing
Hardware Proj Mgmt Expnumeric13 unique values
0 missing
Software Proj Mgmt Expnumeric18 unique values
0 missing
No Of Hardware Proj Estimatednumeric10 unique values
0 missing
No Of Software Proj Estimatednumeric14 unique values
0 missing
Domain Expnumeric10 unique values
0 missing
Procurement Industry Expnumeric9 unique values
0 missing

62 properties

75
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
15
Number of numeric attributes.
0
Number of nominal attributes.
3.68
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.05
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
3.79
Third quartile of skewness among attributes of the numeric type.
6.79
Maximum skewness among attributes of the numeric type.
0.59
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
6.6
Third quartile of standard deviation of attributes of the numeric type.
14.24
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
4.06
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
1.54
First quartile of means among attributes of the numeric type.
10.87
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
3.16
Mean of means among attributes of the numeric type.
1.84
First quartile of skewness among attributes of the numeric type.
0.86
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
2.1
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.2
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
5.9
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.71
Mean skewness among attributes of the numeric type.
1.79
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
4.84
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.39
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
2.27
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
4.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
52.5
Maximum kurtosis among attributes of the numeric type.
0.73
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
16.47
Third quartile of kurtosis among attributes of the numeric type.
11.47
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

5 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: ABS((TotalEstimates-TotalActual)/TotalActual)
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: ABS((TotalEstimates-TotalActual)/TotalActual)
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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