Please visit, subscribe and share 10 Minutes Lectures in Computer Science

# 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, overfitting in non-parametric machine learning algorithms, decision tree, linear regression

## Machine learning Quiz Questions - Set 25

1. Which of the following is a disadvantage of non-parametric machine learning algorithms?

a) Capable of fitting a large number of functional forms (Flexibility)

b) Very fast to learn (Speed)

c) More of a risk to overfit the training data (Overfitting)

d) They do not require much training data

 Answer: (c) More of a risk to overfit the training data Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. For example, decision trees are a nonparametric machine learning algorithm that is very flexible and is subject to overfitting training data. [For more, please refer Overfitting and Underfitting With Machine Learning Algorithms]

2. A decision tree has low training error and a large test error. What is the possible problem?

a) Decision tree is too shallow

b) Learning rate too high

c) There is too much training data

d) Decision tree is overfitting

Answer: (d) Decision tree is overfitting

### Overfitting causes low training error. Overfitting means that the model predicts the (training) data too well. It is too good to be true. If the new data point comes in, the prediction may be wrong.

Pruning can help in reducing the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.

3. Suppose we have a regularized linear regression model. What is the effect of increasing λ on bias and variance?

a) Increases bias, increases variance

b) Increases bias, decreases variance

c) Decreases bias, increases variance

d) Decreases bias, decreases variance

 Answer: (b) Increases bias, decreases variance Increasing λ increases bias and decreases variance Regularized regression It is a type of regression where the coefficient estimates are constrained to zero. The magnitude (size) of coefficients, as well as the magnitude of the error term are penalized. Complex models are discouraged, primarily to avoid overfitting. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. [For more refer here – regularized regression, ] and [Refer here - regularization ] Type of regularized regression Ridge regression (L2 regularization) Lasso regression (L1 regularization)

**********************