When we have to predict the value of a categorical (or discrete) outcome we use logistic regression. I believe we use linear regression to also predict the value of an... moreWhen we have to predict the value of a categorical (or discrete) outcome we use logistic regression. I believe we use linear regression to also predict the value of an outcome given the input values.
Then, what is the difference between the two methodologies?
This question came to my mind while working on 2 projects in AI and ML. What If I'm building a model (e.g. Classification Neural Network,K-NN, .. etc) and this model uses some... moreThis question came to my mind while working on 2 projects in AI and ML. What If I'm building a model (e.g. Classification Neural Network,K-NN, .. etc) and this model uses some function that includes randomness. If I don't fix the seed, then I'm going to get different accuracy results every time I run the algorithm on the same training data. However, If I fix it then some other setting might give better results.
Is averaging a set of accuracies enough to say that the accuracy of this model is xx % ?
I'm not sure If this is the right place to ask such a question/open such a discussion. less
Given a vector of scores and a vector of actual class labels, how do you calculate a single-number AUC metric for a binary classifier in the R language or in simple English?
Page... moreGiven a vector of scores and a vector of actual class labels, how do you calculate a single-number AUC metric for a binary classifier in the R language or in simple English?
Page 9 of "AUC: a Better Measure..." seems to require knowing the class labels, and here is an example in MATLAB where I don't understand
R(Actual == 1))
Because R (not to be confused with the R language) is defined a vector but used as a function?