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.