# baserush
Stable base modeling made quick and easy.
`baserush` is an easy-to-use regression pipeline for preprocessing, optimizing, and summarizing machine learning models within the `scikit-learn` framework. This package is ideal for efficiently building and comparing **stable models** from different model types.
### Supported Model Types
<u>Linear Models</u>
* LinearRegression
* Lasso
* Ridge
* SGDRegressor
<u>Neighbors Models</u>
* KNeighborsRegressor
* RadiusNeighborsRegressor
<u>CaRT Models</u>
* DecisionTreeRegressor
* ExtraTreeRegressor
<u>Ensemble Models</u>
* RandomForestRegressor
* GradientBoostingRegressor
* ExtraTreesRegressor
* RandomTreesEmbedding
## Package Modules
- `preprocess`: missing values, skewness, standardization, and categorical transformations
- `optimize`: automatic feature selection; hyperparameter analysis
- `summary`: training and validation R-Squared, stability tools; model-specific outputs
---
## `preprocess`ing Features
- `simputer` makes it simple to flag and impute missing values.
- Quickly alleviate skewness with `transtorm`.
- Use `simple_scaler` to seamlessly standardize features.
- Efficiently prepare categorical data for modeling with `catcoder`.
## `optimize`-ation Features
- Base modeling made easy with
- `quick_lm` (with automated feature selection)
- `quick_tree`, (includes very fast automated hyperparameter tuning)
- `quick_neighbors`, (automatically tunes n neighbors)
- Use `tuning_results` to analyze the top n-models after hyperparameter tuning
with GridSeachCV | RandomizedSearchCV.
## `summary` Features
`lr_summary`, `tree_summary`, and `knn_summary`
- Automatically instantiate customizable training and validation sets.
- Generate a dataset of model summaries for easy comparison, including:
* Model Name
* Model Class
* Model Type
* R-Squared (Training Set)
* R-Squared (Validation Set)
* Train-Test Gap
* Model-Specific:
* Model Coefficients
* Feature Importance
* Hyperparameter Values
---
## Installation
Install using pip:
```bash
pip install baserush
```
---
## Example Usage
```python
print("Examples coming soon.")
```
---
## License
MIT License. See [LICENSE](LICENSE) for details.
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"description": "# baserush\n\n\n\n\nStable base modeling made quick and easy.\n\n`baserush` is an easy-to-use regression pipeline for preprocessing, optimizing, and summarizing machine learning models within the `scikit-learn` framework. This package is ideal for efficiently building and comparing **stable models** from different model types.\n\n### Supported Model Types\n\n<u>Linear Models</u>\n * LinearRegression\n * Lasso\n * Ridge\n * SGDRegressor\n\n<u>Neighbors Models</u>\n * KNeighborsRegressor\n * RadiusNeighborsRegressor\n\n<u>CaRT Models</u>\n * DecisionTreeRegressor\n * ExtraTreeRegressor\n\n<u>Ensemble Models</u>\n * RandomForestRegressor\n * GradientBoostingRegressor\n * ExtraTreesRegressor\n * RandomTreesEmbedding\n\n\n## Package Modules\n\n- `preprocess`: missing values, skewness, standardization, and categorical transformations\n- `optimize`: automatic feature selection; hyperparameter analysis\n- `summary`: training and validation R-Squared, stability tools; model-specific outputs\n\n\n---\n\n## `preprocess`ing Features\n\n- `simputer` makes it simple to flag and impute missing values.\n- Quickly alleviate skewness with `transtorm`.\n- Use `simple_scaler` to seamlessly standardize features.\n- Efficiently prepare categorical data for modeling with `catcoder`.\n\n## `optimize`-ation Features\n- Base modeling made easy with\n - `quick_lm` (with automated feature selection)\n - `quick_tree`, (includes very fast automated hyperparameter tuning)\n - `quick_neighbors`, (automatically tunes n neighbors)\n\n- Use `tuning_results` to analyze the top n-models after hyperparameter tuning\n with GridSeachCV | RandomizedSearchCV. \n\n\n## `summary` Features\n`lr_summary`, `tree_summary`, and `knn_summary`\n- Automatically instantiate customizable training and validation sets.\n- Generate a dataset of model summaries for easy comparison, including:\n * Model Name\n * Model Class\n * Model Type\n * R-Squared (Training Set)\n * R-Squared (Validation Set)\n * Train-Test Gap\n * Model-Specific:\n * Model Coefficients\n * Feature Importance\n * Hyperparameter Values\n\n---\n\n## Installation\n\nInstall using pip:\n\n```bash\npip install baserush\n```\n\n---\n\n## Example Usage\n\n```python\nprint(\"Examples coming soon.\")\n```\n\n---\n\n## License\n\nMIT License. See [LICENSE](LICENSE) for details.\n",
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