In machine learning, the mean squared error (MSE) is used to evaluate the performance of a regression model. In regression models, the RMSE is used as a metric to measure model performance and the MSE score is used to evaluate the performance. In this article, I will introduce you to the mean squared error in machine learning and its implementation using Python.
What is Mean Squared Error?
In classification problems, the accuracy score is used as a measure to calculate the performance of a model and the confusion matrix is used to evaluate the model. Just like classification, in regression problems, the RMSE score is used as a metric to measure performance and the mean squared error (MSE) is used to evaluate the performance of a regression model.
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The mean squared error (MSE) determines the distance between the set of points and the regression line by taking the distances from the set of points to the regression line and then swapping them. Distances are nothing but errors. Squaring is only done to remove negative values and to give more weight to larger differences.
If the MSE score value is smaller it means you are very close to determining the best fit line which also depends on the data you are working on, so sometimes it may not be possible to get a small MSE score value.
Mean Squared Error using Python
I hope you now have understood what is mean squared error in machine learning. Now let’s have a quick look at how to implement it using the Python programming language. I will start by importing the necessary Python libraries and the dataset to get started with the task of calculating the MSE score using Python:
So here, I am using the diabetes dataset provided by Scikit-learn, let’s simply split the data and train a linear regression model:
So below is how to calculate the MSE score using Python by using the ‘mean_squared_error’ method provided by Scikit-learn:
y_predict = linreg.predict(x_test) print(mean_squared_error(y_test, y_predict))
The MSE score is used to evaluate the performance of a machine learning model while working on regression problems. When the distance is higher it represents a high error rate and when the distance is lower then you are near to the best fit line. I hope you liked this article on Mean Squared Error in machine learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below.