The correct implementation of the mean squared error (MSE) metric is:

```python
def MSE(real_target, predicted_target):
return np.mean((real_target - predicted_target)**2)
```

This function calculates the MSE by subtracting the predicted target from the real target, squaring the result (to eliminate any negative values), and then taking the mean of these squared differences. This gives a single value that represents the average squared difference between the real and predicted targets, which is what the MSE metric is intended to measure.

Question

The correct implementation of the mean squared error (MSE) metric is:

```python
def MSE(real_target, predicted_target):
    return np.mean((real_target - predicted_target)**2)
```

This function calculates the MSE by subtracting the predicted target from the real target, squaring the result (to eliminate any negative values), and then taking the mean of these squared differences. This gives a single value that represents the average squared difference between the real and predicted targets, which is what the MSE metric is intended to measure.

Knowee AI · Accepted Answer

The correct implementation of the mean squared error (MSE) metric is:

```python
def MSE(real_target, predicted_target):
    return np.mean((real_target - predicted_target)**2)
```

This function calculates the MSE by subtracting the predicted target from the real target, squaring the result (to eliminate any negative values), and then taking the mean of these squared differences. This gives a single value that represents the average squared difference between the real and predicted targets, which is what the MSE metric is intended to measure.

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