Machine Learning with Python! Mean Squared Error (MSE)

Tutorial on how to calculate the mean squared error of model predictions. Learn different methods of calculating the mean squared error, graphing the prediction errors of a model and iterating over a parameter to minimize the MSE of a model.

Please note that the MSE formula notation at the top of the notebook is incorrect. 1/N is not included. The notebook on GitHub has been updated with the correction.

Also note the MSE was not calculated for the decision tree model predictions, but you can easily test it using the notebook by following the GitHub link below.

GitHub repo containing the notebook under "Machine Learning with Python"
GitHub Repo: https://github.com/ad17171717/YouTube-Tutorials/tree/main/Machine%20Learning%20with%20Python/Mean%20Squared%20Error

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|-Video Chapters-|
0:00 - Intro
0:23 - Definition of Mean Squared Error (MSE)
1:45 - Loading a data set
2:19 - Exploratory data analysis
3:38 - Modeling the data set
4:51 - Calculating the error of the predictions
6:17 - Calculating MSE
10:58 - Graphing the error of predictions
17:34 - Minimizing MSE by iterating over a parameter value
22:30 - References and additional learning