What is meant by VC dimension of a classifier?
The VC dimension of a classifier is defined by Vapnik and Chervonenkis to be the cardinality (size) of the largest set of points that the classification algorithm can shatter [1].
Is naive Bayes good for high dimensional data?
Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.
Can a naive Bayes classifier make the correct classification?
While naive Bayes often fails to produce a good estimate for the correct class probabilities, this may not be a requirement for many applications. For example, the naive Bayes classifier will make the correct MAP decision rule classification so long as the correct class is more probable than any other class.
How to calculate the VC-dimension of a feature?
Your features are in R. Every classifier has at least dimension 1. VC-Dimension 2: It can classify all four situations correctly. class (0) = True, class (42) = True => a = − 1, b = 1337 classifies this correctly. VC-Dimension 3: No, that doesn’t work. Imagine the classes true and false being ordered like True False True.
How is the VC dimension of a classifier determined?
For a configuration of N points, there are 2^N possible assignments of positive or negative, so the classifier must be able to properly separate the points in each of these. In the below example, we show that the VC dimension for a linear classifier is at least 3, since it can shatter this configuration of 3 points.
What are features in multivariate Bernoulli naive Bayes model?
Bernoulli naive Bayes. In the multivariate Bernoulli event model, features are independent booleans (binary variables) describing inputs. Like the multinomial model, this model is popular for document classification tasks, where binary term occurrence features are used rather than term frequencies.