SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Class:
20. Utility - utility rating - enumerated
Relevant papers
Bulluch B. T., (1992) Eucalyptus Species Selection for Soil Conservation in Seasonally Dry Hill Country - Twelfth Year Assessment New Zealand Journal of Forestry Science 21(1): 10 - 31 (1991)
Kirsten Thomson and Robert J. McQueen (1996) Machine Learning Applied to Fourteen Agricultural Datasets. University of Waikato Research Report
https://www.cs.waikato.ac.nz/ml/publications/1996/Thomson-McQueen-96.pdf + the original publication:
Figure 13: Dataset description for eucalyptus.
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.
Figure 14: Dataset description for wine.
**PC1 Software defect prediction** One of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.
Attribute Information
1. loc : numeric % McCabe's line count of code  
2. v(g) : numeric % McCabe "cyclomatic complexity"  
3. ev(g) : numeric % McCabe "essential complexity"  
4. iv(g) : numeric % McCabe "design complexity"  
5. n : numeric % Halstead total operators + operands  
6. v : numeric % Halstead "volume"  
7. l : numeric % Halstead "program length"  
8. d : numeric % Halstead "difficulty"  
9. i : numeric % Halstead "intelligence"  
10. e : numeric % Halstead "effort"  
11. b : numeric % Halstead  
12. t : numeric % Halstead's time estimator  
13. 10Code : numeric % Halstead's line count  
14. 10Comment : numeric % Halstead's count of lines of comments  
15. 10Blank : numeric % Halstead's count of blank lines  
16. 10CodeAndComment : numeric  
17.uniq_0p : numeric % unique operators  
18.uniq_0pnd : numeric % unique operands  
19.total_0p : numeric % total operators  
20.total_0pnd : numeric % total operands  
21 branchCount : numeric % of the flow graph  
22 branchCount : numeric % of the flow graph  
23 defects : {false, true} % module has/has not one or more reported defects
Relevant papers
- Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013)  
Data Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.  
- Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Pol? 2004 IEEE Conference on High Assurance Software Engineering.  
- T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors Software Defects", Workshop on Predictive Software Models, Chicago
Figure 15: Dataset description for pc1.
Airlines Dataset Inspired in the regression dataset from Elena Ikonomovska. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.
Figure 16: Dataset description for airlines.
Figure 17: Dataset description for jungle_chess_2pcs_raw_endgame Complete.
Figure 18: Dataset description for Kaggle_health-insurance-lead-prediction/raw-data.
Figure 19: Dataset description for Kaggle_pharyngitis.
Dataset Description
In this competition your task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.
File and Data Field Descriptions
train.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.
PassengerId - A unique Id for each passenger. Each Id takes the form gggg.pp where gggg indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.
HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.
CryoSleep - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins
Cabin - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.
Destination - The planet the passenger will be debarking to.
Age - The age of the passenger.
VIP - Whether the passenger has paid for special VIP service during the voyage.
RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.
Name - The first and last names of the passenger.
Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.
Figure 20: Dataset description for kagglespaceship-titanic.