| Name | deeptlf JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | Deep Tabular Learning Framework |
| upload_time | 2024-10-29 16:40:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2024 Vadim Borisov Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
deep-learning
machine-learning
neural-networks
tabular-data
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# DeepTLF: A Framework for Enhanced Deep Learning on Tabular Data
## Overview
**DeepTLF** significantly outperforms traditional Deep Neural Networks (DNNs) in handling tabular data. Using our novel TreeDrivenEncoder, we transform complex, heterogeneous data into a format highly compatible with DNNs. This enables a 19.6% average performance increase compared to conventional DNNs.
## Installation
You can install DeepTLF directly from PyPI:
```bash
pip install deeptlf
```
## Quick Start
Seamlessly integrate DeepTLF into your workflow through its scikit-learn-compatible API:
```python
from deeptlf import DeepTFL
# Initialize and train model
dtlf_model = DeepTFL(n_est=23, max_depth=3, drop=0.23, n_layers=4, task='class')
dtlf_model.fit(X_train, y_train)
# Make predictions
dtlf_y_hat = dtlf_model.predict(X_test)
```
## Features
- Transforms heterogeneous data into DNN-friendly format
- Supports multimodal learning
- Adheres to the scikit-learn API for effortless integration
- Features advanced options like custom layers, dropout rates, and more
## Citation
To cite DeepTLF in your work:
```bib
@article{borisov2022deeptlf,
title={DeepTLF: robust deep neural networks for heterogeneous tabular data},
author={Borisov, Vadim and Broelemann, Klaus and Kasneci, Enkelejda and Kasneci, Gjergji},
journal={International Journal of Data Science and Analytics},
pages={1--16},
year={2022},
publisher={Springer}
}
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"description": "# DeepTLF: A Framework for Enhanced Deep Learning on Tabular Data\n\n\n\n## Overview\n\n**DeepTLF** significantly outperforms traditional Deep Neural Networks (DNNs) in handling tabular data. Using our novel TreeDrivenEncoder, we transform complex, heterogeneous data into a format highly compatible with DNNs. This enables a 19.6% average performance increase compared to conventional DNNs.\n\n## Installation\n\nYou can install DeepTLF directly from PyPI:\n\n```bash\npip install deeptlf\n```\n\n## Quick Start\n\nSeamlessly integrate DeepTLF into your workflow through its scikit-learn-compatible API:\n\n```python\nfrom deeptlf import DeepTFL\n\n# Initialize and train model\ndtlf_model = DeepTFL(n_est=23, max_depth=3, drop=0.23, n_layers=4, task='class')\ndtlf_model.fit(X_train, y_train)\n\n# Make predictions\ndtlf_y_hat = dtlf_model.predict(X_test)\n```\n\n## Features\n- Transforms heterogeneous data into DNN-friendly format\n- Supports multimodal learning\n- Adheres to the scikit-learn API for effortless integration\n- Features advanced options like custom layers, dropout rates, and more\n\n\n## Citation\nTo cite DeepTLF in your work:\n```bib\n@article{borisov2022deeptlf,\n title={DeepTLF: robust deep neural networks for heterogeneous tabular data},\n author={Borisov, Vadim and Broelemann, Klaus and Kasneci, Enkelejda and Kasneci, Gjergji},\n journal={International Journal of Data Science and Analytics},\n pages={1--16},\n year={2022},\n publisher={Springer}\n}\n",
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