Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, lasso regression, tuning parameter, penalty parameter
Machine learning Quiz Questions - Set 27
1. In Lasso regression, if the tuning parameter (lambda) increases ______ increases.
a) Variance
b) Bias
c) Both variance and bias
d) Neither variance nor bias
Answer: (b) Bias The regularization (tuning or penalty) parameter (lambda) is an input to your model. Lambda is the tuning parameter that controls the bias-variance tradeoff and we estimate its best value via cross-validation. The regularization parameter reduces over-fitting, which reduces the variance of your estimated regression parameters; however, it does this at the expense of adding bias to your estimate. Increasing lambda results in less over-fitting but also greater bias. Large values of lambda pull weight parameters to zero leading to large bias. It leads to under-fitting. |
2. In Lasso regression, if the tuning parameter (lambda) decreases ______ increases.
a) Variance
b) Bias
c) Both variance and bias
d) Neither variance nor bias
Answer: (a) Variance Small values of λ allow model to become finely tuned to noise leading to large variance. It leads to over-fitting. |
3. “Less important parameters goes close to zero when we increase the value of tuning parameters” in which of the following regressions?
a) Ridge
b) Lasso
c) Both ridge and lasso
Answer: (b) Lasso With Lasso, when we increase the value of Lambda the most important parameters shrink a little bit and the less important parameters goes closed to zero. So, Lasso is able to exclude silly parameters from the model. |
Related links:
List the type of regularized regression
Multiple choice quiz questions in machine learning
What is bias-variance tradeoff
over-fitting and under-fitting in lasso regression
What is the role of penalty parameter
How tuning parameter lambda affect the performance of lasso regression
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