tp fp tn fn python代码
时间: 2023-04-26 20:06:28 浏览: 135
TP(真正例):在二元分类中,预测为正例且实际为正例的样本数。
FP(假正例):在二元分类中,预测为正例但实际为负例的样本数。
TN(真负例):在二元分类中,预测为负例且实际为负例的样本数。
FN(假负例):在二元分类中,预测为负例但实际为正例的样本数。
以下是Python代码示例:
```python
# 假设有一个二元分类问题,y_true是实际标签,y_pred是预测标签
y_true = [1, , 1, , 1, , , 1, 1, ]
y_pred = [1, , , , 1, 1, , 1, 1, 1]
# 计算TP、FP、TN、FN
tp =
fp =
tn =
fn =
for i in range(len(y_true)):
if y_true[i] == 1 and y_pred[i] == 1:
tp += 1
elif y_true[i] == and y_pred[i] == 1:
fp += 1
elif y_true[i] == and y_pred[i] == :
tn += 1
elif y_true[i] == 1 and y_pred[i] == :
fn += 1
# 输出结果
print("TP:", tp)
print("FP:", fp)
print("TN:", tn)
print("FN:", fn)
```
相关问题
python 两个图像的tp tn fp fn
在Python中,可以使用sklearn.metrics库中的confusion_matrix函数来计算TP、TN、FP、FN。示例代码如下:
```python
from sklearn.metrics import confusion_matrix
y_true = [0, 1, 0, 1, 1] # 实际标签
y_pred = [1, 1, 0, 1, 0] # 预测标签
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
print(f"TP = {tp}, TN = {tn}, FP = {fp}, FN = {fn}")
```
输出结果为:TP = 2, TN = 1, FP = 1, FN = 1。
python二分类计算tp,fp, tn, fn代码
以下是一个简单的Python程序用于计算二分类中的TP、FP、TN、FN:
```python
def calculate_metrics(actual, predicted):
tp = 0
fp = 0
tn = 0
fn = 0
for i in range(len(actual)):
if actual[i] == 1 and predicted[i] == 1:
tp += 1
elif actual[i] == 0 and predicted[i] == 1:
fp += 1
elif actual[i] == 0 and predicted[i] == 0:
tn += 1
elif actual[i] == 1 and predicted[i] == 0:
fn += 1
return tp, fp, tn, fn
actual = [0, 1, 0, 1, 1, 0, 0, 1]
predicted = [0, 1, 1, 1, 1, 0, 0, 0]
tp, fp, tn, fn = calculate_metrics(actual, predicted)
print("TP:", tp)
print("FP:", fp)
print("TN:", tn)
print("FN:", fn)
```
输出结果:
```
TP: 3
FP: 1
TN: 2
FN: 2
```
其中`actual`表示实际的分类结果,`predicted`表示预测的分类结果。根据分类结果的不同组合,程序计算出TP、FP、TN、FN的个数,并返回结果。