I'm implementing prediction system for young cricketers in ODI format using Naive Bayes classifier. The output of the system is to predict whether the young player is rising star or not. I have collected data from statsguru API of espncricinfo but I'm getting only about 300 records of players from ODI. Is it very small for training dataset?
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
I think 300 is a good enough size. Try doing split validation and see what kind of results you are getting. I implemented a naive bayes classifier with a dataset of 100 rows and the results were not too bad. My application was text classification but try for your data and let's see how the accuracy is. Of course you will need training data, so you may have to create some data rows first by classifying them yourself.