Administrative information

This lecture will introduce students to the fundamentals of the hyperparameter tuning. We will use the Census Dataset as the examples of the use and outcomes from tuning various hypermeters. The Adult Census dataset is a binary classification problem. More on this dataset in the corresponding tutorial. The goal of this lecture is to introduce several hyperparameters with examples of how modifying these hyperparameters may aid or hinder learning. In addition we provide examples of under and overfitting, nose and performance gains (training time and in some cases accuracy/loss) when each of the hyperparameters are tunned. We will use diagnostic plots to evaluate the effect of the hyperparameter tunning and in particular a focus on loss, where it should be noted that the module we use to plot the loss is matplotlib.pyplot, thus the axis are scaled. This can mean that significant differences may appear not significant or vice versa when comparing the loss of the training or test data. In addition some liberties for scaffolding are presented, such as the use of Epochs first (almost as a regularization technique) while keeping the Batch size constant. Ideally these would be tunned together, but for this lecture they are separated.