April 2018
Intermediate to advanced
334 pages
10h 18m
English
Logistic regression is a classifier algorithm. Here, we try to predict the probability of the output classes. The class with the highest probability becomes the predicted output. The error between the actual and predicted output is calculated using cross-entropy and minimized through backpropagation. Check the following diagram for binary logistic regression and multi-class logistic regression. The difference is based on the problem statement. If the unique number of output classes is two then it's called binary classification, if it's more than two then it's called multi-class classification. If there are no hidden layers, we use the sigmoid function for the binary classification and we get the architecture ...