CST383 - Module 6
What did I learn in the sixth week of CST383? This week covered topics going over KNN regression, assessing regressors, linear regression, and hyperparameter tuning. The main theme behind them all was to understand how to not only build a model but how to measure whether it works or how it can be improved. The most surprising aspect this week was how linear regression finds the best fitting line. Basically, the idea that training in context to the data is essentially finding the coefficients that produce the lowest possible MSE was both interesting and difficult, as it requires understanding why squaring the errors rather than taking their absolute values leads to a solution that the model arrives at. A concept I found confusing was the computing of both MSE and RMSE. I understood from the lecture material that RMSE is more interpretable in comparison to MSE as it shares the same units as the target variable, but is there a specific situation where MSE is preferred in a real world data...