MS-DS Master of Data science Supervised Learning Algorithms 3

Question 1

What is the primary advantage of gradient boosting over a single decision tree?

Accepted Answer

It sequentially corrects errors of previous models, reducing bias

Answer

Gradient boosting builds an ensemble by sequentially fitting new trees to the residual errors of the combined model so far, progressively reducing bias.

Question 2

In linear regression, what does the coefficient of determination (R²) measure?

Accepted Answer

The proportion of variance in the target explained by the model

Answer

R² measures the fraction of the total variance in the dependent variable that is captured by the model's predictions, ranging from 0 to 1.

Question 3

Which criterion is most commonly used to measure node impurity in classification trees?

Accepted Answer

Gini impurity

Answer

Gini impurity measures the probability of misclassifying a randomly chosen element and is the default splitting criterion in most classification tree implementations.

Question 4

A Naive Bayes classifier assumes which key property about features?

Accepted Answer

Features are conditionally independent given the class label

Answer

Naive Bayes applies Bayes' theorem with the 'naive' assumption that each feature is conditionally independent of every other feature given the class label.

Question 5

What is the effect of increasing the regularization strength (C parameter) in an SVM?

Accepted Answer

The margin widens and misclassifications are penalized less

Answer

In SVM, a smaller C allows more margin violations (softer margin), widening the margin and reducing sensitivity to individual training points.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Supervised Learning Algorithms 3