Machine Learning Machine Learning 4

Question 1

What is the difference between generative and discriminative models?

Accepted Answer

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

Answer

Generative models learn the joint distribution of inputs and labels to model how data is generated, while discriminative models directly learn the boundary between classes.

Question 2

What is the elbow method used for in clustering?

Accepted Answer

Determining the optimal number of clusters by identifying where inertia reduction slows

Answer

The elbow method plots within-cluster inertia against the number of clusters (k) and looks for a kink where adding more clusters yields diminishing returns.

Question 3

Which of the following best describes 'data leakage' in a machine learning pipeline?

Accepted Answer

Information from outside the training set improperly influencing model training, leading to overly optimistic evaluation

Answer

Data leakage occurs when information from the test set or future data influences model training or preprocessing, causing inflated performance metrics that don't generalize.

Question 4

What is the purpose of dropout in neural network training?

Accepted Answer

To randomly deactivate neurons during training to prevent co-adaptation and reduce overfitting

Answer

Dropout randomly sets a fraction of neuron activations to zero during each training step, forcing the network to learn redundant representations and reducing overfitting.

Question 5

In gradient boosting, what does the 'learning rate' (shrinkage) control?

Accepted Answer

The contribution of each tree to the final prediction, scaling down its effect

Answer

The learning rate in gradient boosting scales the contribution of each new tree, helping prevent overfitting by requiring more trees to achieve the same fit.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Machine Learning 4