multinomialnb and bernoullinb have single parameteralphawhich controls model complexity the way alpha works is that the algorithm adds to the data alpha many virtual data points that have positive values for all the features this results in "smoothingof the statistics large alpha means more smoothingresulting in less complex models the algorithm' performance is relatively robust to the setting of alphameaning that setting alpha is not critical for good performance howevertuning it usually improves accuracy somewhat gaussiannb is mostly used on very high-dimensional datawhile the other two variants of naive bayes are widely used for sparse count data such as text multinomialnb usually performs better than binarynbparticularly on datasets with relatively large number of nonzero features ( large documentsthe naive bayes models share many of the strengths and weaknesses of the linear models they are very fast to train and to predictand the training procedure is easy to understand the models work very well with high-dimensional sparse data and are relatively robust to the parameters naive bayes models are great baseline models and are often used on very large datasetswhere training even linear model might take too long decision trees decision trees are widely used models for classification and regression tasks essentiallythey learn hierarchy of if/else questionsleading to decision these questions are similar to the questions you might ask in game of questions imagine you want to distinguish between the following four animalsbearshawkspenguinsand dolphins your goal is to get to the right answer by asking as few if/else questions as possible you might start off by asking whether the animal has feathersa question that narrows down your possible animals to just two if the answer is "yes,you can ask another question that could help you distinguish between hawks and penguins for exampleyou could ask whether the animal can fly if the animal doesn' have feathersyour possible animal choices are dolphins and bearsand you will need to ask question to distinguish between these two animals--for exampleasking whether the animal has fins this series of questions can be expressed as decision treeas shown in figure - in[ ]mglearn plots plot_animal_tree( supervised learning