[Solved] COMP30027 Assignment1-Naive Bayes main

# supervised-learning

To run Gaussian naive Bayes classifier, run the code blocks for the following functions: preprocess, train, predictand evaluate. Then call each functions (example showed at the end of the noetbook).1.Change the file location of preprocess by location of training set, pass the result into train (could name it train_set).2.Pass the result of train to predict first argument (could name it train_info), change the second argumentinto testing set location, the third argument is the smoothing number used for GaussianNB (should be a negative number with large absolute value),the default one is -50. This hyperparameter can be turned.3.Pass the result of predict to evaluate (could name it pred), function evaluate will print the accuracy.

To run KDE naive Bayes classifier, run the code blocks for Question 5. Then call each functions (example showed at the end of the noetbook).1.Change the file location of preprocess by location of training set, pass the result into train_KDE (could name it train_set).2.Pass the result of train_KDE to pred_KDE first argument (could name it train_info_KDE), change the second argumentinto testing set location, the third argument is the kernel bandwidth, the default is 10, it could be turned (5-25 is recommended).The fourth argument is smoothing number used for KDENB (should be a negative number with large absolute value),the default one is -50. This hyperparameter also can be turned.3.Pass the result of predict to evaluate (could name it pred_KDE), function evaluate will print the accuracy.

To see the Macro-Micro metrics result, run the code blocks for Question 1.Then call each functions (example showed at the end of the noetbook).1. Change the first argument of predictionTable into the result of Naive Bayes predict function and pass itinto macroEvaluation or microEvaluation (could name it table).2. Result of prediction table is a confusion matrix and is printed.