File size: 3,158 Bytes
c35cbf5 d19bd1a c35cbf5 71233eb c35cbf5 d19bd1a c35cbf5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
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`: Path to the PrismRCL executable for classification
- `naivebayes`: Specifies Naive Bayes as the training evaluation method
- `rclticks=18`: Sets the number of RCL iterations during training to 18
- `boxdown=1`: Configuration parameter for training behavior
- `channelpick=5`: RCL training parameter
- `data=C:\path\to\Haberman-Survival\train_data`: Path to the training data for Haberman Survival classification
- `testdata=C:\path\to\Haberman-Survival\test_data`: Path to the testing data for evaluation
- `savemodel=C:\path\to\models\mymodel.classify`: Path to save the resulting trained model
- `log=C:\path\to\log_files`: Directory path for storing log files of the training process
- `stopwhendone`: Instructs PrismRCL to end the session once training is complete
## 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.
|