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

KNN is a lazy learner that stores the entire training set and computes distances to all points during prediction, making it computationally expensive for large datasets.

Question 2

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

Accepted Answer

Mean Squared Error (MSE)

Answer

CART uses Mean Squared Error to evaluate regression splits, choosing the split that minimizes the variance of the target variable within each resulting node.

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

The sigmoid (logistic) function transforms any real-valued number into a value between 0 and 1, making it suitable for probability estimation.

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

Adding more trees to a Random Forest reduces variance through averaging but does not significantly affect bias, and the improvement plateaus after a sufficient number of trees.

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

Logistic regression naturally outputs calibrated probabilities, provides interpretable coefficients indicating feature importance, and requires relatively little hyperparameter tuning.

Data Science Practice Test

Data Science Practice Test

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