In this thread, you will learn different ways and methods of computing the mean squared error of true and predicted series. The mean squared error (MSE) is a common metric used to measure the accuracy of predictions made by regression models. It measures the average squared difference between the predicted values and the true values of a dataset. Here are the few easiest and efficient methods of computing this metric:

#### 1. Using NumPy library:

- We can use the NumPy’s
`np.square()`

method to first square all the errors between the true and predicted values. - Then, NumPy’s
`np.mean()`

method to calculate the mean of all the squared errors.

#### 2. Using Panda's library:

- We can simply use Panda’s
`mean()`

function to calculate the mean of all the squared errors which are calculated using a simple mathematical expression.

#### 3. Using Sk-learn library:

- This library has a built-in function of
`mean_squared_error()`

which calculates the MSE by just passing the series of true and predicted values as arguments.