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 primary purpose of feature scaling in machine learning?
Answer: To ensure all features contribute equally to model training
Feature scaling normalizes feature ranges so that no single feature dominates model training due to its magnitude.
Which technique handles missing values by replacing them with the average of the column?
Answer: Mean imputation
Mean imputation replaces missing values with the column's average, preserving the dataset's overall distribution.
What does one-hot encoding do to categorical variables?
Answer: Creates binary columns for each category
One-hot encoding converts each category into a separate binary column, avoiding the assumption of ordinal relationships.
Which dimensionality reduction technique projects data onto directions of maximum variance?
Answer: PCA
PCA (Principal Component Analysis) finds orthogonal axes of maximum variance to compress feature dimensions.
What is the primary benefit of feature selection in a machine learning pipeline?
Answer: Reduces overfitting and training time
Feature selection removes irrelevant or redundant features, reducing overfitting risk and computational cost.
Which outlier detection method uses the interquartile range (IQR) to flag extreme values?
Answer: Tukey's fence method
Tukey's fence method flags values beyond 1.5x IQR from Q1 or Q3 as outliers for removal or treatment.