Dark Mode
IEEE-CIS Model Blending Results
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This data product focuses on providing submission-style files relevant to the IEEE-CIS Fraud Detection competition context. Its primary purpose is to facilitate the blending of model predictions, offering a structured approach to combining outputs from different public or produced solutions. The data represents the outcomes of successful predictive efforts.
Columns
The files generally contain two key columns:
- TransactionID: A unique identifier used to link the prediction back to the original transaction details.
- isFraud: A numerical label, typically representing the predicted probability or classification result (0 or 1). Statistical analysis shows the mean value for this column is approximately 0.03, indicating a significant imbalance where non-fraudulent transactions are dominant.
Distribution
The data is typically provided in CSV format, ready for easy integration into analytical pipelines. An example file has a size of around 14.83 MB. The data structure includes 507,000 valid records, ensuring 100% data availability across the defined columns. The TransactionID values range from approximately 3.66 million to 4.17 million.
Usage
Ideal applications for this data include enhancing the accuracy of fraud detection systems by averaging or stacking model outputs. It is highly useful for running final-stage ensemble learning techniques and for benchmarking the performance of individual machine learning models against blended results.
Coverage
The data pertains specifically to the context and identifiers established by the IEEE-CIS Fraud Detection challenge. While specific geographic or time range details are inherited from the primary challenge data, the expected update frequency for these blend files is annual, reflecting typical competition cycles or model refinement timelines.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For developing and testing advanced ensemble techniques like boosting, stacking, or voting classifiers.
- Machine Learning Engineers: For quickly implementing and testing performance improvements via model blending.
- Researchers: Studying the effects of blending high-performing models in skewed financial fraud detection environments.
Dataset Name Suggestions
IEEE-CIS Model Blending Results
Fraud Detection Submission File Collection
Transaction Prediction Ensemble Data
Attributes
Original Data Source: IEEE-CIS Model Blending Results
QUALITYLoading...