classification_report(zero_division=False)
时间: 2023-09-15 07:20:02 浏览: 125
The `classification_report()` function is a utility function from the scikit-learn library that generates a text report showing the main classification metrics for a given set of predictions and true labels. These metrics include precision, recall, F1-score, and support for each class.
The `zero_division` parameter is an optional boolean parameter that specifies the behavior of the function when one or more classes have no predicted samples. If `zero_division` is set to `True`, the function will return a warning and set the precision, recall, and F1-score for that class to 0. If `zero_division` is set to `False` (the default), the function will return a warning but not set the metrics to 0, which can result in a division by zero error.
For example, the following code generates a classification report for a set of predictions and true labels, with `zero_division` set to `False`:
```
from sklearn.metrics import classification_report
y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]
print(classification_report(y_true, y_pred, zero_division=False))
```
This will output the following report:
```
precision recall f1-score support
0 0.67 1.00 0.80 2
1 0.00 0.00 0.00 2
2 0.50 1.00 0.67 2
accuracy 0.58 6
macro avg 0.39 0.67 0.49 6
weighted avg 0.39 0.58 0.45 6
C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass zero_division=False as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {0} as keyword args. From version 0.25 "
```
Note that the warning is generated because `zero_division` was passed as a positional argument instead of a keyword argument. To avoid the warning, we can pass it as a keyword argument:
```
print(classification_report(y_true, y_pred, zero_division=True))
```
This will include the support for each class even if there were no predicted samples for that class.
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