Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

Related MCQs

• Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
• 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.
• Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?
• In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
• In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
• Following is powerful distance metrics used by Geometric model
• Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps? Note: You have more than 1000 models predictions 1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set. 2. Start with empty ensemble 3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set
• If machine learning model output involves target variable then that model is called as
• If machine learning model output doesnot involves target variable then that model is called as
• A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong? Note: Model has successfully deployed and no technical issues are found at client side except the model performance