I’m quite dumbfounded by the comments on this thread. If I make a python library where I take three points as input and spit out the area of the triangle you ge

I’m quite dumbfounded by the comments on this thread. If I make a python library where I take three points as input and spit out the area of the triangle you get when you join those points with straight lines, I have to ensure that said area is correct. Even a high school kid knows that. To argue that I will always assume that the three points come from a right triangle, so I will compute three distances and return half the product of the two smaller ones, that’s just...what’s the polite thing to say in HN circles these days...retarded on my part. It means I’m completely braindead. It’s means I’m dense. Obtuse. It basically means I need more coffee. I don’t get to argue that its a right triangle library, so I will only entertain points from a right triangle. Because once you put the library out there, anybody might and will use it. Who do you think uses Excel ? The vast majority are Secretaries, housewives, florists, dog walkers...not just the quants sitting in a bank. If some middle school kid doing his homework calls my library with three collinear points, I must throw an exception. Or atleast spit out an area of zero. Imagine how you’d feel as a parent or a teacher if your kid got a big fat zero because I assume two of the three lines are perpendicular when it’s trivial to verify that they aren’t. As the library author, I must do the right thing, simply because, hey it’s a nice planet & so let’s be nice to fellow citizens and library callers & so forth. Ethics, etc. Let me ask you this. It’s fucking 2019. You have a+bx on your rhs. Why the fuck are you setting g(p) = log(p/(1-p)) as your link ? You say oh it has a nice interpretation, it’s log odds...ok so are you in epi ? You care about lives and deaths, cures & placebos that sort of thing ? Nope, you are doing ML for some faang. Ok then why logit ? You can pick literally any CDF. Every continuous cdf is smooth sigmoid. They are all cadlag. The finance community generally picks the gaussian cdf, so that’s called probit and all their papers are full of probit regression. Whereas the healthcare guys don’t have the analytical chops, so they just settle on logistic regression. But it’s 2019! You can stick any cdf in there !!! You know, glm used to be a graduate statistics subject. Nowadays sophomores are happily using glm with a custom link function. 16-17 year olds. They craft a nice looking smooth cdf, get it’s closed form, use that in the glm link function. Everything fits so nicely, you get tight CIs, good inference. When these kids graduate, if they know you are still debating about probit vs logit with a default l2 penalty, they are going to laugh in your face & kick you to the curb. You guys are elite programmers. glms must be your favorite playpen. Craft some custom link functions and step up to the plate. logistic with default l2?! jesus.