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In the following project, we examined the effects of hyperparameter tuning on six different classification models: logistic regression, decision tree, support vector machine (SVM), AdaBoost, random forest and kernel SVM.

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NadiaBlostein/COMP551_ML_project_2

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DIRECTORY STRUCTURE:

acllmdb/ 
	# IMDb dataset
imdb/
	imdb_adaboost.ipynb # script to run IMDB data through adaboost classifier
	imdb_decision_tree.ipynb # script to run IMDB data through decision tree classifier
	imdb_kernel_SVM.ipynb # script to run IMDB data through kernel SVM classifier
	imdb_log_reg.ipynb # script to run IMDB data through logistic regression classifier
	imdb_random_forest.ipynb # script to run IMDB data through random forest classifier
	imdb_SVM.ipynb # script to run IMDB data through SVM classifier

newsgroup/
	newsgroup_adaboost.ipynb # script to run Newsgroup data through adaboost classifier
	newsgroup_decision_tree.ipynb # script to run Newsgroup data through decision tree classifier
	newsgroup_kernel_SVM.ipynb # script to run Newsgroup data through kernel SVM classifier
	newsgroup_log_reg.ipynb # script to run Newsgroup data through logistic regression classifier
	newsgroup_random_forest.ipynb # script to run Newsgroup data through random forest classifier
	newsgroup_SVM.ipynb # script to run Newsgroup data through SVM classifier

results/
	contains sub-directories with graphs and output .txt files per dataset, per model

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In the following project, we examined the effects of hyperparameter tuning on six different classification models: logistic regression, decision tree, support vector machine (SVM), AdaBoost, random forest and kernel SVM.

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