https://github.com/niklasvonm/ensemblelearning
Python package for optimal aggregation of bagged ensemble models
https://github.com/niklasvonm/ensemblelearning
ensemble-model machine-learning
Last synced: 2 months ago
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Python package for optimal aggregation of bagged ensemble models
- Host: GitHub
- URL: https://github.com/niklasvonm/ensemblelearning
- Owner: NiklasvonM
- License: mit
- Created: 2023-04-09T07:23:54.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-15T22:08:49.000Z (over 1 year ago)
- Last Synced: 2025-01-27T05:48:17.382Z (4 months ago)
- Topics: ensemble-model, machine-learning
- Language: Python
- Homepage:
- Size: 78.1 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://github.com/NiklasvonM/EnsembleLearning/actions/workflows/mypy.yml)
[](https://github.com/NiklasvonM/EnsembleLearning/actions/workflows/ruff.yml)
[](https://github.com/NiklasvonM/EnsembleLearning/actions/workflows/tests.yml)
[](https://opensource.org/licenses/MIT)
[](https://makeapullrequest.com)# EnsembleLearning
## Usage
```python
from ensemble_learning.local_weights import local_weightspredictions = [1, 3, 5, 7, 9]
target = 5weights = local_weights(predictions, target)
print("Weights:", weights)
```## Examples
There is an example demonstrating how to use the module with time series forecasting models, such as ARIMA, Exponential Smoothing, and Prophet. To run the example, you need to install additional dependencies:
```bash
pip install ensemble-learning[examples]
python examples/time_series_local_weights.py
```