In assignment 4 you shall implement the Naïve Bayes machine learning algorithm and use it on some datasets. It can be implemented in any programming language you like. You can work alone or in group of two students. You shall present your application and code at an oral examination.

Grade	Requirements
E	Implement the Naïve Bayes algorithm, using the code structure below (you are allowed to add more classes and methods if needed) A good guide on how to implement the algorithm can be found here Train the model on the Iris and Banknote authentication datasets (see Datasets page) Calculate classification accuracies for both datasets (use all data for both training and testing) You can verify your results with the results in Web ML Experimenter
C-D	Implement code for generating confusion matrices, using the code structure below
A-B	Implement code for n-fold cross-validation, using the code structure below It shall be possible to use 3, 5 or 10 folds (it is okay if your implementation supports other folds) Calculate accuracy score for 5-fold cross-validation on both datasets You can verify your results with the results in Web ML Experimenter (note that the cross-validation results can differ a bit due to differences in train-test splits)

 

Code structure requirements

NaiveBayes class
void fit ( X:float[][], y:int[] )	Trains the model on input examples X and labels y
preds:int[] predict ( X:float[][] )	Classifies examples X and returns a list of predictions

Other methods
accuracy:float accuracy_score ( preds:int[], y:int[] )	Calculates accuracy score for a list of predictions
conf_matrix:int[][] confusion_matrix ( preds:int[], y:int[] )	Generates a confusion matrix and returns the result as an integer matrix
preds:int[] crossval_predict ( X:float[][], y:int[], folds:int )	Runs n-fold cross-validation and returns a list of predictions