AML Model Evaluation & Optimization Techniques 3

Question 1

Which regularization technique explicitly prevents co-adaptation of neurons by randomly zeroing activations during training?

Accepted Answer

Dropout

Answer

Dropout randomly deactivates neurons during each training step, forcing the network to learn redundant representations and preventing co-adaptation.

Question 2

In the context of learning curves, what does a large gap between training and validation loss with both curves converging indicate?

Accepted Answer

High variance (overfitting) needing more regularization or data

Answer

A large persistent gap between training and validation loss with convergence indicates the model has memorized training data and is not generalizing well (high variance/overfitting).

Question 3

What does the Brier Score measure in probabilistic classification?

Accepted Answer

The mean squared error between predicted probabilities and actual binary outcomes

Answer

The Brier Score is the mean squared error between predicted class probabilities and the true binary (0/1) outcomes, rewarding well-calibrated probability estimates.

Question 4

Which cross-validation strategy is most appropriate when time-series data exhibits temporal dependencies?

Accepted Answer

Time series split with expanding window

Answer

Time series split uses an expanding or sliding window where the validation set always comes after the training set chronologically, preserving temporal causality.

Question 5

What is the effect of using a very small batch size in stochastic gradient descent?

Accepted Answer

Higher gradient noise that can escape local minima but slower wall-clock convergence

Answer

Small batch sizes produce noisy gradient estimates that can help escape sharp local minima but typically require more iterations and wall-clock time to converge.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Model Evaluation & Optimization Techniques 3