Small amount of training data set for naive Bayes classifier for binary classification
Actually in machine learning more data equals more accuracy but as you mentioned in the question you had 300 sample dataset.So, the classifier has the little room to decide whom it should select but if you have less number of classes and features you may get better results.When I'm doing my project based on sensor data I used just about 100 sample records from a sensor and it actually predicted very well and got the accuracy of 77 and it has grown once I increased the dataset.but don't train it with the noise like outliers and unwanted features they affect your accuracy a lot