For this assignment, I will focus on understanding and applying classification metrics used to evaluate predictive models. These metrics include accuracy, precision, recall, specificity, and the F1 score.
My approach will be to review how each metric is calculated and what type of classification performance it measures. I will consider example classification scenarios to understand how these metrics behave when class distributions are imbalanced.
Potential challenges include interpreting metric tradeoffs and selecting the most appropriate metric for a given problem. These challenges will be addressed by comparing metrics across different classification outcomes and reviewing confusion matrix results.