# How to compute mean squared error (MSE) for a predictive model in Python?

I’m currently working on a project that involves computing the mean squared error (MSE) of a predictive model. I have a basic understanding of what MSE represents, it is a metric that measures the average squared difference between the predicted and the true values of a dataset, still, I’m facing some difficulties in calculating it properly. I’m not sure about the formula to use, the variables to consider, and the steps to follow in Python. If anyone could provide some guidance or a step-by-step explanation of how to compute MSE, I would greatly appreciate it.

• 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.

@mubashir_rizvi, you can simply use Panda’s `mean()` function to calculate the mean of all the squared errors which are calculated using a simple mathematical expression. Let’s see the example given below for a better understanding:

The above code calculates the Mean Squared Error (MSE) between two Pandas series, `y_true` and `y_pred`, by subtracting them, squaring the differences, then taking the mean of the resulting values, and storing the result.
I hope it clears up your confusion!

Hey @mubashir_rizvi , the scikit-learn (sklearn) library has a built-in function called `mean_squared_error()` that calculates the mean squared error (MSE) between two series of true and predicted values. To use this function, you can pass the two series as arguments, with the true values as the first argument and the predicted values as the second argument, and it will return the MSE as a float value. Here is the code below for your better understanding:

I hope the above explanation helps you.