which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

Related MCQs

• which of the following step / assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most.
• Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?
• Which one of the following is suitable? 1. When the hypothsis space is richer, overfitting is more likely. 2. when the feature space is larger , overfitting is more likely.
• 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
• Suppose we fit �Lasso Regression� to a data set, which has 100 features (X1,X2�X100).� Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),� and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Suppose we fit �Lasso Regression� to a data set, which has 100 features (X1,X2�X100).� Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),� and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100).  Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100).  Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Which of the following can help to reduce overfitting in an SVM classifier?