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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. Correct Answer In case of very large lambda; bias is high, variance is low

Related Questions

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
04. When lambda goes to infinity, we get very, very large coefficients approaching infinity.
The frequency of tuning fork A is greater than the tuning fork C by 2% whereas the frequency of tuning fork B is 3% less than the standard tuning fork C. Both A and B produce 6 beats/sec. find the frequency of tuning fork A.
A tuning fork produces 4 beats/s when sounded with a tuning fork of frequency 512Hz. The same tuning fork, when sounded with a another tuning fork of frequency 514Hz, produces 6 beats/s. Find the frequency of the tuning fork.
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
We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
Two monochromatic waves having frequencies $$\omega $$ and $$\omega + \Delta \omega \left( {\Delta \omega \ll \omega } \right)$$    and corresponding wavelengths $$\lambda $$ and $$\lambda - \Delta \lambda \left( {\Delta \lambda \ll \lambda } \right)$$    of same polarization, travelling along X-axis are superimposed on each other. The phase velocity and group velocity of the resultant wave are respectively given by
Let’s say we can hear a beat frequency of 2Hz from two tuning forks. The first tuning fork has a frequency of 100Hz. If wax is applied on the 2nd tuning fork, the beat frequency becomes 4Hz, what is the frequency of the 2nd tuning fork?
Which of the following conditions must be satisfied for a transistor to be in saturation? 1. Its collector to base junction should be under forward bias 2. Its collector to base junction should be under reverse bias 3. Its emitter to base junction should be under reverse bias 4. Its emitter to base junction should be under forward bias Which of the above conditions are correct?
We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?