This is a scoring measure to test the effectiveness of the classification model. It measures the amount of discrepancy between the predicted probability and the actual label. The lower the loss value in the log, the higher the perfection of the model. For an ideal model, log log = 0. For example, since the precision is — this is the number of correct predictions, ie the prediction that corresponds to the actual label, the Log Loss value — it is a measure of the uncertainty of our projected labels based on how it differs from the actual label.
where, N: no. of samples. M: no. of attributes. y ij : indicates whether i th sample belongs to j th class or not. p ij : indicates probability of i th sample belonging to j th class.
LogLoss implementation using sklearn
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, develo...
A recipe for having fun and getting things done with the Raspberry Pi ...
It would be easy for me to develop native apps using Java, C++ or Objective-C and I am also able to learn Kotlin, Dart or Swift, but things are much easier when you just use Python. I have done a Djan...
Python for Programmers: with Big Data and Artificial Intelligence Case Studies This book, written for programmers with a high-level experience in another language, uses how-to instructions to teach...