Which of the following is true about bagging?
1. Bagging can be parallel
2. The aim of bagging is to reduce bias not variance
3. Bagging helps in reducing overfitting

Related MCQs

• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
• Which of the following is / are true about weak learners used in ensemble model? 1. They have low variance and they don’t usually overfit 2. They have high bias, so they can not solve hard learning problems 3. They have high variance and they don’t usually overfit
• 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.
• 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?
• Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?
• 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.
• 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.
• Which of the following can help to reduce overfitting in an SVM classifier?