![]() The Wikipedia page on discretization also has some useful references. You can read more about discretization in. You can employ any discretization technique including simple techniques that divide the values of a feature into partitions of K equal intervals or K equal frequencies. In Part 1, you will discretize the features. The features in the CM1 dataset are all continuous variable. defects : module has/has not one or more reported defects.lOBlank : Halstead's count of blank lines.lOComment : Halstead's count of lines of comments.n : Halstead total operators + operands. ![]() Each observation consists of 21 features and a class variable listed below. This dataset consists observations about 498 software modules. The dataset is available in ARFF (for use with Weka) and CSV formats.ĬM1 is a NASA spacecraft instrument written in C. ![]() You will use the CM1 dataset publically available from the PROMISE Software Engineering Repository. Of implementation, and documentation) will receive 20% bonus points (measured based on multiple factors including the f1 score, quality Treat this as a competition! The best best implementation In this project, you will implement a Naive Bayes (NB) classifier to predict whether a software module is likely to have defects or not.
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