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# 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, k-nearest neighbor, decision tree, linear regression

## Machine learning Quiz Questions - Set 32

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1. Which of the following is true about regularized linear regression model?

a) Increase in regularization parameter (lambda) will make the model to underfit the data and the validation error will go up.

b) Decrease in regularization parameter (lambda) will make the model to overfit the data and the training error go up

c) Increase in regularization parameter (lambda) will make the model to underfit the data and the training error go down

d) All of the above are true

 Answer: (a) Increase in regularization parameter (lambda) will make the model to underfit the data and the validation error will go up. Regularization parameter (tuning parameter) λ, used in the regularization techniques, controls the impact on bias and variance. As the value of λ rises, it reduces the value of coefficients and thus reducing the variance. Till a point, this increase in λ is beneficial as it is only reducing the variance (hence avoiding overfitting), without loosing any important properties in the data. But after certain value, the model starts loosing important properties, giving rise to bias in the model and thus underfitting. [Refer here for more.]

2. Which of the following is a characteristic of decision tree?

a) High variance

b) High bias

c) Smoothness of prediction surfaces

d) Low variance

 Answer: (a) High variance A model has high variance if it is very sensitive to (small) changes in the training data. Decision trees are generally unstable considering that a small change in the data set can result in a very different set of splits. This results in high variance. This is mainly due to the hierarchical nature of decision trees, since a change in split points in the initial stages will affect all the subsequent splits.

3. Let us consider single-link and complete-link hierarchical clustering. In which of these approaches, it is possible for a point to be closer to points in other clusters than the points in its own cluster?

a) It is possible in single-link clustering

b) It is possible in complete-link clustering

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### Why decision trees are prone to high variance?

What is regularized linear regression model?