For Beginners Flashcards

What is 'feature engineering' in a machine learning workflow?

Answer: The process of creating or transforming input variables to improve model performance

Feature engineering transforms raw data into informative representations that help models learn patterns more effectively.

Which algorithm is best described as building an ensemble of decision trees trained on random data subsets?

Answer: Random Forest

Random Forest trains many decision trees on bootstrap samples and aggregates their predictions to reduce variance and improve accuracy.

In AI model deployment, what is an API endpoint used for?

Answer: Exposing model inference capabilities as a network-accessible interface

An API endpoint accepts input data over a network, runs it through the model, and returns predictions to the calling application.

What is 'gradient descent' designed to minimize?

Answer: A loss function measuring prediction error

Gradient descent iteratively moves model parameters in the direction that most steeply decreases the loss function.

Which of the following best describes an AI 'pipeline'?

Answer: A sequence of data processing and modeling steps chained together end-to-end

An AI pipeline connects steps like data ingestion, preprocessing, model training, evaluation, and deployment into a reproducible workflow.

What is the primary purpose of normalization (e.g., min-max scaling) applied to input features?

Answer: To bring features onto a comparable scale so no single feature dominates training

Normalization prevents features with large numerical ranges from disproportionately influencing gradient updates during training.

What is 'model inference' in the context of a deployed AI system?

Answer: Running a trained model on new inputs to generate predictions

Inference is the production-time step where a frozen, trained model processes new inputs and returns predictions without any weight updates.