用python写一下MSE
时间: 2023-10-01 11:04:52 浏览: 80
Sure! MSE(Mean Squared Error)是用来评估预测值与真实值之间差异的一种方法,它的公式如下:
MSE = (1/n) * sum((y_true - y_pred)^2)
其中,y_true表示真实值,y_pred表示预测值,n表示样本数量。
下面是用Python实现MSE的代码:
```python
import numpy as np
def mse(y_true, y_pred):
return np.mean(np.square(y_true - y_pred))
```
使用方法如下:
```python
y_true = np.array([1, 2, 3, 4, 5])
y_pred = np.array([1.5, 2.5, 3.5, 4.5, 5.5])
print(mse(y_true, y_pred)) # 输出结果为 0.25
```
希望对你有所帮助!
相关问题
python MSE
MSE stands for Mean Squared Error. It is a commonly used metric to measure the average squared difference between the predicted and actual values in regression tasks. In Python, you can calculate MSE using various libraries such as NumPy or scikit-learn.
Here's an example of calculating MSE using NumPy:
```python
import numpy as np
def calculate_mse(y_true, y_pred):
mse = np.mean((y_true - y_pred) ** 2)
return mse
# Example usage
y_true = np.array([1, 2, 3, 4, 5])
y_pred = np.array([1.2, 2.5, 2.8, 3.7, 4.9])
mse = calculate_mse(y_true, y_pred)
print("MSE:", mse)
```
In this example, `y_true` represents the true values and `y_pred` represents the predicted values. The `calculate_mse` function calculates the MSE using the formula: MSE = (1/n) * Σ(y_true - y_pred)^2,
python mse
MSE是均方误差(Mean Squared Error)的缩写,是一种常用的衡量回归模型预测精度的指标。它计算预测值与真实值之间差的平方的平均值。在Python中,可以使用numpy库来计算MSE。例如,假设我们有一个真实值数组y_true和一个预测值数组y_pred,可以使用以下代码计算MSE:
```python
import numpy as np
mse = np.mean((y_true - y_pred)**2)
```