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---
license: cc-by-4.0
---
# Haberman's Survival Dataset
## Overview
This dataset contains tabular data for classifying survival status of patients who had undergone surgery for breast cancer. Each sample is stored in a separate text file, with features space-separated on a single line. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application or API.
## Dataset Structure
The dataset is organized into the following structure:
Haberman-Survival/
train_data/
class_1/
sample_0.txt
sample_10.txt
...
class_2/
sample_0.txt
sample_10.txt
...
test_data/
class_1/
sample_0.txt
sample_10.txt
...
class_2/
sample_0.txt
sample_10.txt
...
**Note**: All text file names must be unique across all class folders.
## Features
- **Tabular Data**: Each text file contains space-separated values representing the features of a sample.
- **Classes**: There are two classes, each represented by a separate folder:
- `class_1`: Patients who survived 5 years or longer.
- `class_2`: Patients who did not survive 5 years.
## Usage(pre-split; optimal parameters)
Here is an example of how to load the dataset using PrismRCL:
```bash
C:\PrismRCL\PrismRCL.exe naivebayes rclticks=18 boxdown=1 channelpick=5 data=C:\path\to\Haberman-Survival\train_data testdata=C:\path\to\Haberman-Survival\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone
Explanation of Command
C:\PrismRCL\PrismRCL.exe: classification application
chisquared: training evaluation method
rclticks=18: RCL training parameter
boxdown=1: RCL training parameter
channelpick=5 : RCL training parameter
data=C:\path\to\Haberman-Survival\train: path to training data
testdata=C:\path\to\Haberman-Survival\test: path to testing data
savemodel=C:\path\to\models\mymodel.classify: path to save resulting model
log=C:\path\to\log_files: path to logfiles
stopwhendone: ends the PrismRCL session when training is done
```
## License
This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details.
## Original Source
This dataset was originally sourced from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/43/haberman+s+survival). Please cite the original source if you use this dataset in your research or applications.
```
Haberman, S.J. (1976). Generalized Residuals for Log-Linear Models. Proceedings of the 9th International Biometrics Conference, Boston, 1976.
```
## Additional Information
The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.
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