CST 383 - Intro to Data Science | Week 6
Learning Log | Week 6 This week we learned about hyperparameter tuning, KNN regression, linear regression, and how to evaluate regression models. Hyperparameter tuning helped me understand that some model settings are chosen before training and that the best values depend on the dataset. Grid search seems useful because it tests different combinations in an organized way, but I still wonder how to choose a reasonable range of values without testing too many combinations. The difference between KNN regression and linear regression was also interesting. KNN uses nearby training examples to make predictions, while linear regression learns coefficients that describe the relationship between the predictors and the target. Linear regression seems faster when making predictions, but it may struggle when the relationship is not close to linear. The hardest part for me was interpreting regression errors and predicted versus actual plots. I understand that lower MSE and MAE values are better, ...