Week 6 CST 383
This week in CST 383, we continued to supplement our learning about machine learning ideas with a focus on hyperparameter tuning, kNN regression, linear regression, and evaluating or assessing the regressors. I thought this week was very interesting because we built upon the idea of kNN and how it does not entirely require parameters due to being an instance based method. From my understanding, instance based means that it does not automatically learn the best settings to use for training and instead take the training data and make predictions by comparing the new points of data to the already existing examples. In this case it does not guarantee the best refinement for a model and it may not perform well. To ensure that we get the best results and performance we need to apply some hyperparameters. The lecture discussed that we should test different hyperparameters to find the best settings for the kNN modeling, which includes testing different combinations of the neighbor values k, th...