Computing Mean Squared Error

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.