My solution for assignment 4 in Web Intelligence (2DV515) at Linnaeus University

The assingment.

• 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
• Note that you are not required to build a REST web service for this assignment

• Implement the Naïve Bayes algorithm, using the code structure below (you are allowed to add more classes and methods if needed)
• 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)

• Implement code for generating confusion matrices, using the code structure below

• 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

void fit ( X:float[][], y:int[] )

Trains the model on input examples X and labels y.

int[] predict ( X:float[][] )

Classifies examples X and returns a list of predictions.

float accuracy_score ( preds:int[], y:int[] )

Calculates accuracy score for a list of predictions.

int[][] confusion_matrix ( preds:int[], y:int[] )

Generates a confusion matrix and returns the result as an integer matrix.

int[] crossval_predict ( X:float[][], y:int[], folds:int )

Runs n-fold cross-validation and returns a list of predictions.