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Simulated Feature-Based Binary Classification Data
Data Science and Analytics
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About
Simulated data provides a robust foundation for evaluating how machine learning models perform on artificial structures. This collection is generated through random number generation and simulation techniques to represent real-world phenomena distributions. By providing a controlled environment for binary classification tasks, these records allow for the augmentation of limited data and the testing of predictive accuracy when the underlying distribution is known. Using simulation offers a promising solution for training models when finding sufficient natural data is difficult.
Columns
- feature1: A numerical variable with a mean of approximately 0.98 and values ranging from -3.03 to 4.95, representing a simulated independent variable.
- feature2: A numerical variable with a mean of 0.67 and a broader distribution ranging from -10.4 to 12.6, serving as the second independent variable.
- target: The binary classification label, consisting of integer values 0 or 1, representing the two distinct classes for prediction.
Distribution
The information is provided in a CSV file titled
generated_test.csv with a file size of 8.6 kB. It contains 399 valid records across 3 distinct columns. The data maintains a high usability rating of 10.00 and shows 100% validity with no missing or mismatched values. The update frequency is set to never, as it is a fixed simulation output.Usage
This resource is ideal for training and testing binary classification algorithms within a machine learning pipeline. It can be used for benchmarking model performance, practicing feature engineering, or conducting simulation studies to see how different algorithms handle known distributions. Researchers may also use it to experiment with data augmentation techniques.
Coverage
The scope of this data is entirely synthetic and does not represent a specific geographic or temporal range. It consists of 399 instances generated to simulate a balanced distribution between two classes, with 199 records for class 0 and 200 records for class 1.
License
CC0: Public Domain
Who Can Use It
Beginner data scientists can utilise these records to learn the basics of binary classification without the noise of real-world data. Machine learning practitioners can use it for rapid prototyping of models. Additionally, educators in computer science can leverage the simulation-based approach to teach students about statistical distributions and model training.
Dataset Name Suggestions
- Synthetic Binary Classification and Simulation Index
- Simulated Feature-Based Binary Classification Data
- Artificial Binary Distribution for Model Training
- Machine Learning Simulation Benchmark Dataset
Attributes
Original Data Source: Simulated Feature-Based Binary Classification Data
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