sklearn rbf
时间: 2023-08-08 22:13:08 浏览: 46
Sklearn is a popular machine learning library in Python, and "rbf" stands for Radial Basis Function. In the context of sklearn, "rbf" refers to the Radial Basis Function kernel, which is commonly used in support vector machines (SVMs) for classification and regression tasks.
The Radial Basis Function kernel measures the similarity between data points based on their distance in the feature space. It is particularly useful when the data is not linearly separable. The RBF kernel transforms the data into a higher-dimensional space, where it becomes easier to separate the classes.
In sklearn, you can use the RBF kernel by specifying it as the "kernel" parameter in SVM models. For example:
```python
from sklearn import svm
# Create an SVM model with RBF kernel
model = svm.SVC(kernel='rbf')
# Train the model
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
```
This is just a basic example, and there are more parameters and options you can explore with SVM using RBF kernel in sklearn.