Binary classification and key concepts of it Question Title * 1. Choose applications that rely on classification. Fraud detection. All of the above. Image classification. Detecting sentence sentiment. Question Title * 2. In binary classification, what is the number of classes observed? Not possible to say. 3. 2. 1. Question Title * 3. How do we represent class labels to train an algorithm? (Suppose that we have two classes, cat and dog) Just pass class names to the algorithm. Map class names to integers. Map class names to floats. None of the variants. Question Title * 4. What does a logistic regression model estimate? None of the variants. Probabilities of belonging to one of N classes. The name of the class. The number of classes to output. Question Title * 5. What metric to use when we are dealing with imbalanced classes? f1 score. precision. recall. accuracy. Question Title * 6. What is the idea of using cross-validation? To divide model into sub models and test them independently. To test model on all the data. None of the variants. To train and test classifier on different subsets of data and then receive the predefined score for each subset to understand model performance better. Question Title * 7. How should we deal with NaN values in the dataset? Use KNN to impute missing values. Fill NaN values with some unique token or statistics based measure. All the variants. Drop all rows with NaN values. Question Title * 8. What was a goal of function choose_best_classifier? The function trains one classifier multiple times and denotes that it’s the best one. The function applies greedy search to find the best model based on predefined metric. None of the variants. The function randomly selects a classifier and denotes that it’s the best one. Готово