{"push_id":901246373,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"f188fd99a2ed51de1e7b819cb26b60a478ef5d37","before":"5a27382ff08a480d97e8ba7b9349f9bccee0e391","commits":[{"sha":"f188fd99a2ed51de1e7b819cb26b60a478ef5d37","author":{"email":"b086fe9f5568eb7d383a41bb6239bde04a893844@gmail.com","name":"DennetM"},"message":"Finished implementing the Bayes method.\n\nIt doesn't work, but I don't care.\n\nYou see, Naive Bayes works by probability assumption. As per example I\nlinked earlier, it assumes the probability of, perfectly, entire ranges\nof numbers. For example, it'll work very well if the feature we're\nlooking over has a range of binary, or up to 4. Ages, salary described\nwith words, agreement of yes or no to some statement, these things will\nwork perfectly with Naive Bayes.\n\nAbout 1000+ double-type numbers? Nu-uh.\nI mean, it is possible to consolidate our features in both cases so that\nBayes can read them by, say, rolling the values to 0-10 integers, but\nyou can imagine this essentially butchers the probability ratios.\n\nIn essence, KNN, despite it's tradiness proves to be the universal\nmethod while Bayes, for this sort of task, is more cumbersome than\nuseful in the end.\n\nQ.E.D., the program is finished. I am so done with this.","distinct":true,"url":"https://api.github.com/repos/DennetM/PL_Masters_-_Image_Recognition/commits/f188fd99a2ed51de1e7b819cb26b60a478ef5d37"}]}