Machine Learning basics (part 3)
After the last introduction in part 2 about Linear Regression, let’s jump to Logistic Regression along with some basic concepts.
Used to find the best fitted line for logistic regression. Th idea is the same as in linear regression, we keep rotating the line until we find the one with maximum likelihood. First, we need to project data into a x-axis and y-axis (log(odd)). Then we need to transform it back to probability, and calculate log likelihood. We keep doing this until…