- Logistic regression: - R-squared = correlation squared - Linear regression - Sensitivity = TPR = TP/TP+FN - Specificity = TN/TN+FP - AUC: higher, better -> 0.5 means no classification ability - Using 10 engines to identify whether the machine is correctly setting the distance of the coil from the wall -> hypothesis testing - Training the model that will be used to determine which engine should be inspected in greater depth based on the test metrics -> logistic and KNN - Determining which metrics are most closely associated with a faulty engine -> logistic - Determining whether the temperature measured during the simple test is an intrinsic characteristic of the engine or a function of the environment in which the test is done -> simulation - Once you have interviewed engineers and collected a whole list of simple metrics, determining which of these many metrics to use in your faultprediction model -> logistic, training and test set - Using a model to determine which engine should be inspected in greater depth based on the test metrics -> AUC - Using a model to determine which metrics are most closely associated with a faulty engine -> p-values - Estimating the total number of recalls that will occur -> Calibration curves On average, user 1 tends to be an “easier rater”, and to give higher scores overall -> ru On average, news article 1 tends to be rated more highly by users than others -> No var Question 7 On average, longer news articles are rated more highly than shorter ones -> little t Question 8 On average, user 1 prefers longer news articles, whereas user 2 prefers shorter news articles -> no
