try an 'l1' penalty with hinge loss, or 'l1' penalty with squared hinge loss. Does 'l2' work with the squared hinge loss function?
时间: 2024-01-14 17:04:56 浏览: 79
Outside-the-penalty-function-method.rar_penalty function_penalty
The 'l2' penalty is not compatible with the squared hinge loss function in LinearSVC. The squared hinge loss function is specifically designed to work with the 'l1' penalty.
If you want to use the squared hinge loss function with a penalty, you should use the 'l1' penalty. Here are two examples:
1. 'l1' penalty with hinge loss:
```python
from sklearn.svm import LinearSVC
model = LinearSVC(loss='hinge', penalty='l1')
```
2. 'l1' penalty with squared hinge loss:
```python
from sklearn.svm import LinearSVC
model = LinearSVC(loss='squared_hinge', penalty='l1')
```
Please note that the 'l2' penalty is commonly used with the hinge loss function, but not with the squared hinge loss function.
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