Data Science FREE Data Science Supervised Learning Algorithms Questions and Answers 3

Question 1

What is the main disadvantage of K-Nearest Neighbors (KNN) when applied to large datasets?

Accepted Answer

High computational cost at prediction time because it must compute distances to all training points

Answer

It cannot handle categorical features

Answer

It always underfits the training data

Answer

It requires the data to be normally distributed

Question 2

Which metric does the CART algorithm use by default for regression tree splits?

Accepted Answer

Mean Squared Error (MSE)

Answer

Gini impurity

Answer

Information gain

Answer

Chi-squared statistic

Question 3

In logistic regression, what function maps the linear combination of inputs to a probability between 0 and 1?

Accepted Answer

Sigmoid function

Answer

ReLU function

Answer

Softplus function

Answer

Step function

Question 4

What happens when the number of estimators in a Random Forest is increased significantly?

Accepted Answer

Variance decreases while bias remains roughly constant, with diminishing returns

Answer

Both bias and variance decrease proportionally

Answer

The model increasingly overfits the training data

Answer

Training time decreases due to parallelization

Question 5

Which supervised learning algorithm is most appropriate when you need a probabilistic interpretation of class membership and feature importance with minimal tuning?

Accepted Answer

Logistic Regression

Answer

Deep Neural Network

Answer

K-Nearest Neighbors

Answer

Support Vector Machine with RBF kernel

Question 6

What is the purpose of pruning in decision tree algorithms?

Accepted Answer

To reduce overfitting by removing branches that provide little predictive power

Answer

To increase the depth of the tree for better accuracy

Answer

To add more features to each split

Answer

To convert the tree into an ensemble model