Short answer, if you’re only interested in solving a prediction task: use Naive Bayes. A Bayesian network (has a good wikipedia page) models relationships between features in a very general way. If you know what these relationships are, or have enough data to derive them, then it may be appropriate to use a Bayesian network. … Read more
There’s actually a very good example on Wikipedia: In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. For example, a fruit may be considered to be an apple if … Read more
To save: import pickle f = open(‘my_classifier.pickle’, ‘wb’) pickle.dump(classifier, f) f.close() To load later: import pickle f = open(‘my_classifier.pickle’, ‘rb’) classifier = pickle.load(f) f.close()
In my experience, properly trained Naive Bayes classifiers are usually astonishingly accurate (and very fast to train–noticeably faster than any classifier-builder i have everused). so when you want to improve classifier prediction, you can look in several places: tune your classifier (adjusting the classifier’s tunable paramaters); apply some sort of classifier combination technique (eg, ensembling, … Read more
The accepted answer has many elements of k-NN (k-nearest neighbors), a different algorithm. Both k-NN and NaiveBayes are classification algorithms. Conceptually, k-NN uses the idea of “nearness” to classify new entities. In k-NN ‘nearness’ is modeled with ideas such as Euclidean Distance or Cosine Distance. By contrast, in NaiveBayes, the concept of ‘probability’ is used … Read more