{"id":8,"title":"Иннов ИС non_longes","description":null,"category":"Programming","source_filename":"non_longest_answer_test.txt","questions":[{"text":"Which statement correctly describes the difference between classification and regression?","explanation":null,"position":1,"is_review_required":false,"answer_text":null,"images":[],"options":[{"text":"Classification predicts continuous values, while regression predicts categories","is_correct":false,"position":1,"images":[]},{"text":"Classification predicts categories, while regression predicts continuous values","is_correct":true,"position":2,"images":[]},{"text":"Both classification and regression always predict only binary labels","is_correct":false,"position":3,"images":[]},{"text":"Regression is used only for unsupervised learning","is_correct":false,"position":4,"images":[]}]},{"text":"Which of the following is an example of a regression 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workflow","is_correct":false,"position":4,"images":[]}]},{"text":"What usually happens when the number of neighbors in KNN increases?","explanation":null,"position":4,"is_review_required":false,"answer_text":null,"images":[],"options":[{"text":"The model becomes more complex and unstable","is_correct":false,"position":1,"images":[]},{"text":"The model becomes smoother and simpler","is_correct":true,"position":2,"images":[]},{"text":"The model stops using distance","is_correct":false,"position":3,"images":[]},{"text":"The model becomes a clustering algorithm","is_correct":false,"position":4,"images":[]}]},{"text":"Which is an advantage of KNN?","explanation":null,"position":5,"is_review_required":false,"answer_text":null,"images":[],"options":[{"text":"It never requires data preprocessing","is_correct":false,"position":1,"images":[]},{"text":"It is simple and easy to interpret","is_correct":true,"position":2,"images":[]},{"text":"It always works best with thousands of 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for?","explanation":null,"position":21,"is_review_required":false,"answer_text":null,"images":[],"options":[{"text":"Visualizing the hierarchy of clusters","is_correct":true,"position":1,"images":[]},{"text":"Showing logistic regression coefficients","is_correct":false,"position":2,"images":[]},{"text":"Scaling numerical data","is_correct":false,"position":3,"images":[]},{"text":"Testing Random Forest parallelism","is_correct":false,"position":4,"images":[]}]},{"text":"What can DBSCAN identify in addition to clusters?","explanation":null,"position":22,"is_review_required":false,"answer_text":null,"images":[],"options":[{"text":"Regression coefficients","is_correct":false,"position":1,"images":[]},{"text":"Noise or outlier points","is_correct":true,"position":2,"images":[]},{"text":"Principal components","is_correct":false,"position":3,"images":[]},{"text":"Feature importance from trees","is_correct":false,"position":4,"images":[]}]},{"text":"In DBSCAN, what does epsilon 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