python基于BGDT输出召回率和f1score
时间: 2024-05-07 22:20:01 浏览: 24
可以使用sklearn库中的classification_report函数来输出召回率和f1 score。具体步骤如下:
1. 导入所需库和数据:
```python
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
```
2. 构建BGDT模型并进行训练:
```python
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
```
3. 在测试集上进行预测并输出召回率和f1 score:
```python
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
```
输出结果如下:
```
precision recall f1-score support
0 0.97 0.97 0.97 39
1 0.98 0.98 0.98 75
accuracy 0.98 114
macro avg 0.98 0.98 0.98 114
weighted avg 0.98 0.98 0.98 114
```
其中,precision表示精度,recall表示召回率,f1-score表示F1值。可以看到,BGDT模型在测试集上的召回率和f1 score均较高。
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