metrics/recall(B)
时间: 2024-01-24 10:04:31 浏览: 110
Recall (also known as sensitivity) is a metric used in binary classification that measures the proportion of actual positive cases that are correctly identified as positive by the model. It is calculated as the number of true positives divided by the sum of true positives and false negatives. The formula for recall in class B is:
recall(B) = true positives(B) / (true positives(B) + false negatives(B))
相关问题
import precision_recall_curve()
`precision_recall_curve()` 是 Python 中 scikit-learn 库的一个函数,用于计算精确度-召回率曲线。该曲线是二分类问题中评估模型性能的常用工具,特别是在不平衡数据集中。它可以帮助我们了解分类器在不同阈值设置下的表现。
使用 `precision_recall_curve()` 函数时,你需要提供真实的标签(ground truth labels)和模型预测的概率(不是预测的类别)。函数会返回三个数组:精确度(precision)、召回率(recall)和阈值(thresholds)。通过这些值,你可以绘制出精确度-召回率曲线,进而评估模型在不同决策阈值下的表现。
以下是使用 `precision_recall_curve()` 函数的一个简单示例:
```python
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
# 假设 y_true 是真实的标签,y_scores 是模型预测的概率
y_true = [0, 1, 1, 0, 1]
y_scores = [0.1, 0.4, 0.35, 0.8, 0.7]
precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
plt.plot(thresholds, precision[:-1], 'b--', label='precision')
plt.plot(thresholds, recall[:-1], 'g--', label='recall')
plt.xlabel('Threshold')
plt.legend()
plt.show()
```
在这个例子中,`precision` 和 `recall` 数组的长度会比 `thresholds` 长一个,因为在最后一个阈值时,精确度和召回率会分别变为样本中正类的比例和 1.0。因此,我们在绘图时使用 `[:-1]` 来确保所有的数据点都对应相同的阈值。
Classification metrics can't handle a mix of binary and continuous targets
This statement is partially true. Classification metrics, such as accuracy, precision, recall, and F1 score, are designed to evaluate the performance of models that predict categorical targets, such as binary (0/1) or multi-class (e.g., A/B/C).
If the target variable is continuous, such as in regression problems, different metrics are used, such as mean squared error (MSE), mean absolute error (MAE), and R-squared.
However, in some cases, the target variable may have a mix of binary and continuous values, which requires a different approach. For example, in medical diagnosis, a model may predict the probability of a disease (continuous value) and then classify patients as having the disease or not based on a threshold (binary value). In such cases, hybrid metrics such as area under the receiver operating characteristic curve (AUC-ROC) and precision-recall curve (AUC-PR) can be used to evaluate the model's performance.
In summary, while classification metrics are not suitable for evaluating models that predict continuous targets, there are hybrid metrics that can handle a mix of binary and continuous targets.