Which metric can you use to evaluate a classification model?
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A. B. C. D.A
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classificationThe correct answer to the question "Which metric can you use to evaluate a classification model?" is A. true positive rate.
Explanation: Classification models are used to predict the class of an observation based on the values of its features. To evaluate the performance of a classification model, we need to compare the predicted classes with the actual classes of the observations. There are several metrics that can be used to evaluate a classification model, such as accuracy, precision, recall, F1 score, and true positive rate.
True positive rate (TPR), also known as sensitivity or recall, is the proportion of positive cases that are correctly identified by the model. TPR is calculated as the number of true positives divided by the sum of true positives and false negatives:
TPR = true positives / (true positives + false negatives)
A true positive is a case where the model correctly identifies a positive case, and a false negative is a case where the model incorrectly identifies a negative case. TPR is a useful metric when the cost of false negatives is high, such as in medical diagnosis or fraud detection.
Mean absolute error (MAE), coefficient of determination (R2), and root mean squared error (RMSE) are metrics used to evaluate regression models, which are used to predict a continuous variable. MAE is the average absolute difference between the predicted values and the actual values, R2 is a measure of how well the model fits the data, and RMSE is the square root of the average squared difference between the predicted values and the actual values. These metrics are not appropriate for evaluating classification models, which predict discrete classes.