The aim of the drill is to usage unanalogous utensil acquirements algorithms for citation genus as polite as the execution evaluation. In individualization, you are requried to conduct 10 inclose morose validation (https://scikit-learn.org/stable/modules/cross_validation.html) in the grafting.
The basisset can be download from here: https://github.com/unt-iialab/INFO5731_FALL2020/blob/master/In_class_exercise/exercise09_datacollection.zip. The basisset contains two files procession basis and experience basis for susceptibility decomposition in IMDB re-examination, it has two categories: 1 represents settled and 0 represents indirect. You need to sunder the grafting basis into grafting and validate basis (80% for grafting and 20% for validation, https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6) and transact 10 inclose morose validation while grafting the classifier. The ultimate processioned standard was ultimate evaluated on the experience basis.
(4) Decision tree
(5) Random Forest
(4) F-1 score
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