Week 5 CST 383
This week in CST 383 we finally took a look at some core concepts of machine learning. To understand machine learning more practically, we had to shift our focus toward data preprocessing, such as handling missing values and scaling features before applying models. After doing so we took a look at the different classifiers with a large focus on the KNN classification and learning how to train and test splits, undergo cross validation, and identify the different ways to assess the classifiers. Something that stood out to me was realizing that machine learning is not just about choosing a model and training it right away. The preprocessing steps and evaluation choices matter just as much, and if they’re handled incorrectly, the results of the model can be completely misleading and distorted. When engaging with the practicality of data cleaning or preprocessing with our homework assignments, it made me realize that some datasets can be quite messy. Recently we’ve been working with small d...