当试图将朴素贝叶斯：
training_data = sample; %
target_class = K8;
# train model
nb = NaiveBayes.fit(training_data, target_class);
# prediction
y = nb.predict(cluster3);
我得到一个错误：
??? Error using ==> NaiveBayes.fit>gaussianFit at 535
The within-class variance in each feature of TRAINING
must be positive. The within-class variance in feature
2 5 6 in class normal. are not positive.
Error in ==> NaiveBayes.fit at 498
obj = gaussianFit(obj, training, gindex);
任何人都可以揭示出这个光怎么解决呢？ 请注意，我看过一个类似的帖子在这里 ，但我不知道该怎么办？ 看来，如果其试图基于列而不是行，以适应，阶级差异，应根据各行属于特定类的概率。 如果我删除这些列那么它的工作原理，但显然这个心不是我想做的事情。
Assuming that there is no bug anywhere in your code (or NaiveBayes code from mathworks), and again assuming that your training_data is in the form of NxD where there are N observations and D features, then columns 2, 5, and 6 are completely zero for at least a single class. This can happen if you have relatively small training data and high number of classes, in which a single class may be represented by a few observations. Since NaiveBayes by default treats all features as part of a normal distribution, it cannot work with a column that has zero variance for all features related to a single class. In other words, there is no way for NaiveBayes to find the parameters of the probability distribution by fitting a normal distribution to the features of that specific class (note: the default for distribution is normal).
Take a look at the nature of your features. If they seem to not follow a normal distribution within each class, then normal is not the option you want to use. Maybe your data is closer to a multinomial model mn:
nb = NaiveBayes.fit(training_data, target_class, 'Distribution', 'mn');
文章来源: Naive Bayes: the within-class variance in each feature of TRAINING must be positive
