python MSE
时间: 2023-10-15 10:29:05 浏览: 54
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,
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