A multiple regression model has

Related MCQs

• What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn't work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
• What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn’t work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
• For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient of determination is
• In a linear regression problem, we are using �R-squared� to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
• In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
• 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
• A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2 constant), y will
• Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
• Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
• Logistic regression is a ________ regression technique that is used to model data having a _____outcome.