AML Feature Engineering & Data Preprocessing Flashcards
6 cards from real AML practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 AML Feature Engineering & Data Preprocessing flashcards as text
What is the curse of dimensionality in machine learning?
Answer: The phenomenon where data becomes sparse as dimensions increase, degrading model performance
As feature dimensions increase, data points become increasingly sparse, making distance-based algorithms less effective.
Which encoding technique assigns integer values to categories that have a natural order?
Answer: Ordinal encoding
Ordinal encoding maps ordered categories such as low/medium/high to integers that preserve their natural rank.
What is the purpose of a train-validation-test split in model development?
Answer: To tune hyperparameters and evaluate final model performance on unseen data
The validation set enables hyperparameter tuning while the test set provides an unbiased final performance estimate.
Which technique generates synthetic minority class samples to address class imbalance?
Answer: SMOTE
SMOTE creates new synthetic samples by interpolating between existing minority class examples rather than duplicating them.
What does a log transformation primarily help with during feature engineering?
Answer: Reducing the skewness of right-skewed distributions
Log transformation compresses large values and expands small ones, making right-skewed distributions more symmetric.
What is a machine learning data pipeline?
Answer: An automated sequence of data processing steps from ingestion to model-ready format
A data pipeline automates and chains data collection, cleaning, transformation, and feature engineering steps for reproducibility.