The quality of classifiers depends largly on the input data, and how you teach a model.
For the best results, following these guidelines:
Take a random selection of 80% of your examples for teaching, and use the other 20% for validating. You can measure what percentage of your validation set was correctly predicted by the model; this is the model accuracy. You can experiment with different data sets and compare them to decide which gives you the best results.
To avoid a biased model, the order of the examples you teach should be random. Do not teach all examples for each class in a group, instead spread the teaching out among all classes.
A few tools exist to help you train your classifier: