Classify a dataset using different methods

Using a provided dataset of a categories URI, (URI and category), perform three methods to classify the dataset. Code must be in python.

Shuffle the dataset, for each category split randomly to 20-80 testing and training, create 8 fold on the training, where each fold have to have URIs from all the categories, group the testing file together.

Method 1: use naive bayes Binary classifier.

Method2: use naive bayes Binary classifier on each category to determine where the URI is positive (using binary approach to simulate the multiway).

Method3: use Naive Bayes Multiway classifier.

For each method show using tables the following:

1-(category-precision-recall- fscore) for each fold, with the total score and weighted average. Note the weight average is because the dataset is not balanced.

2-the confusion matrix of each fold.

3-(fold number- total precision-total recall-total fscore- total weight average).

Evner: Maskinoplæring, Python

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