# python-scipyHow can I use the Radial Basis Function (RBF) in Python with SciPy?

Radial Basis Function (RBF) is a type of kernel-based machine learning algorithm which can be used for both classification and regression problems. The SciPy library in Python provides a convenient implementation of the RBF algorithm through its `rbf`

function. The following example code shows how to use the `rbf`

function to fit a model to a dataset of points:

```
from scipy.interpolate import Rbf
import numpy as np
# Generate some data
x = np.linspace(0, 10, 10)
y = np.sin(x)
# Fit a radial basis function
rbf = Rbf(x, y)
# Print the fitted model
print(rbf)
```

## Output example

`<scipy.interpolate.rbf.Rbf object at 0x7f9c8bb7d400>`

The code consists of the following parts:

- Importing the
`Rbf`

function from the`scipy.interpolate`

library and the`numpy`

library. - Generating some data points using the
`linspace`

function from`numpy`

. - Fitting a radial basis function to the data points using the
`Rbf`

function from`scipy.interpolate`

. - Printing the fitted model.

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