CST 383: learning log 4
This week we spent a lot of time working with discrete variables and learning how to calculate probabilities from data. Using the heart disease dataset made the ideas feel more concrete. I understand better now why some probabilities make sense for discrete variable while exact value probabilities don’t make sense for continuous variables like blood pressure or something like that. It felt like joint and conditional probability was a big focus this week. Writing conditions with Pandas and then using .mean() to compute probabilities helped me see how there is a connection with math and the actual data. Conditional probability finally feels more intuitive when I think of it as restricting the dataset first and then asking a question within that smaller group. To me personally, contingency tables was one of the more useful things learned this week. Seeing everything organized in a table made it easier to compute marginal, joint, and conditional probabilities without getting lost....