Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

Related MCQs

• Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case? 1. Accuracy metric is not a good idea for imbalanced class problems. 2.Accuracy metric is a good idea for imbalanced class problems. 3.Precision and recall metrics are good for imbalanced class problems. 4.Precision and recall metrics aren’t good for imbalanced class problems.
• Logistic regression is a ________ regression technique that is used to model data having a _____outcome.
• Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
• Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
• How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinary least squares regression?
• Which of the following metrics can be used for evaluating regression models? i) R Squared ii) Adjusted R Squared iii) F Statistics iv) RMSE / MSE / MAE
• Which of the following metrics can be used for evaluating regression models? i) R Squared ii) Adjusted R Squared iii) F Statistics iv) RMSE / MSE / MAE
• Which of the following methods do we use to best fit the data in Logistic Regression?
• Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data
• Which of the following are correct statement(s) about stacking? A machine learning model is trained on predictions of multiple machine learning models A Logistic regression will definitely work better in the second stage as compared to other classification methods First stage models are trained on full / partial feature space of training data