semvec/pypi-packages huggingface dataset:
pyregression 0.0.8
Machine learning regressions methods
Requires Python: >=3.6

# PyRegression

PyRegression is a python library for using in machine learning regressions. The first minimal launched version include nonlinear regression with K-Nearest Neighbor (KNN), Random Forest (RFO) and Support Vector Regression (SVR). Of course, you can use PyRegression for Multiple Linear Regression (MLR) too.

The nonlinear methods include the searching of the best hyperparameters using k-fold cross validation. For more details visit the official documentation in pyregression.gitlab.io

## Install

``pip install pyregression``

or if you prefer (recommended) install in a virtual environment:

```bash
python3 -m venv venv-pyregression
cd venv-pyregression
source bin/activate

pip install pyregression
# install dependencies
pip install pandas
pip install sklearn
```

## Example code

```python
# import regression methods
from pyregression.regressors import MLR, KNN, RFO, SVR

# import dataset example
from pyregression.datasets import load_soilmoisture

# load dataset example
X,y = load_soilmoisture() 

# do the regressions (test_size = 0.6 by default)
mlr = MLR(X,y)
knn = KNN(X,y)
rfo = RFO(X,y)
svr = SVR(X,y)

# print predicted values
for regression in (mlr, knn, rfo, svr):
    print(regression["y_pred"])
```
pyregression-emilopez 0.0.7
Machine learning regressions methods
Requires Python: >=3.6

# PyRegression

PyRegression is a python library for using in machine learning regressions. The first minimal launched version include nonlinear regression with K-Nearest Neighbor (KNN), Random Forest (RFO) and Support Vector Regression (SVR). Of course, you can use PyRegression for Multiple Linear Regression (MLR) too.

The nonlinear methods include the searching of the best hyperparameters using k-fold cross validation. For more details visit the official documentation in pyregression.gitlab.io

## Example code

```python
# import regression methods
from pyregression.regressors import MLR, KNN, RFO, SVR

# import dataset example
from pyregression.datasets import load_soilmoisture

# load dataset example
X,y = load_soilmoisture() 

# do the regressions (test_size = 0.6 by default)
mlr = MLR(X,y)
knn = KNN(X,y)
rfo = RFO(X,y)
svr = SVR(X,y)

# print predicted values
for regression in (mlr, knn, rfo, svr):
    print(regression["y_pred"])
```