Hi, I need some programming done in either Java or C++. The program is a simple classifier, with real-world application.
2 versions of Naïve bayes classifier
- discretize continuous attributes by creating a number of equally sized bins and counting the number of samples (on a per-class basis) that fall into each bin. With these values, you can estimate conditional probabilities. the number of bins used a run-time parameter.
- assume a Gaussian distribution represents the class-conditional probability for continuous attributes. You can then calculate sample means and variances (again on a per-class basis) using the class-conditional probability equation
Bagging / ensemble classifier
bagging algorithm provided to construct 60 datasets from training sets (provided). Data points selected using a random uniform probability with replacement. Each constructed dataset will be used to train on classifier for the final ensemble of 60 classifiers.
Also create decision trees to classify data.
Note, code should not use any pre-built libraries.
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