MS-DS Master of Data science Machine Learning 4

Question 1

What is the purpose of the learning rate in gradient descent optimization?

Accepted Answer

It scales the step size taken in the direction of the negative gradient

Answer

The learning rate is a scalar that multiplies the gradient to determine how large a step is taken toward the loss minimum during each parameter update.

Question 2

Which concept describes the phenomenon where a feature's importance appears artificially low because its effect is shared with correlated features in a model?

Accepted Answer

Multicollinearity

Answer

Multicollinearity among predictors causes their individual coefficients or feature importances to be unstable and potentially misleading because the model cannot isolate their separate contributions.

Question 3

In reinforcement learning, what is the Bellman equation used for?

Accepted Answer

Expressing the value of a state as the immediate reward plus discounted value of successor states

Answer

The Bellman equation decomposes the value function recursively: V(s) = E[r + γV(s')], forming the foundation of dynamic programming and Q-learning.

Question 4

What distinguishes a generative model from a discriminative model?

Accepted Answer

Generative models learn the joint distribution P(X,Y); discriminative models learn P(Y|X) directly

Answer

Generative models model how data is generated (joint distribution), enabling them to synthesize new samples, while discriminative models focus solely on the decision boundary P(Y|X).

Question 5

In XGBoost, what is the role of the 'lambda' (L2) regularization parameter?

Accepted Answer

It penalizes the squared magnitude of leaf weights to prevent overfitting

Answer

The lambda parameter in XGBoost adds an L2 penalty on leaf weight values in the objective function, shrinking them toward zero to reduce model complexity.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Machine Learning 4