Data Science Data Wrangling and Preprocessing 3

Question 1

A feature ranges from 0 to 1,000,000 while another ranges from 0 to 1. Which preprocessing step puts them on a comparable scale?

Accepted Answer

Feature scaling (normalization/standardization)

Answer

Scaling transforms features to comparable ranges so models relying on distance aren't dominated by large-magnitude features.

Question 2

Min-max scaling transforms a feature to which range by default?

Accepted Answer

[0, 1]

Answer

Min-max scaling maps the minimum to 0 and the maximum to 1 using (x - min)/(max - min).

Question 3

Standardization (z-score) rescales a feature to have what properties?

Accepted Answer

Mean 0 and standard deviation 1

Answer

Z-score standardization subtracts the mean and divides by the standard deviation, yielding mean 0 and unit variance.

Question 4

When encoding a nominal categorical variable with no inherent order, which method avoids implying a false ranking?

Accepted Answer

One-hot encoding

Answer

One-hot encoding creates a binary column per category, avoiding the implied ordering that integer label encoding introduces.

Question 5

A right-skewed feature like website session duration is transformed with a log. What is the main benefit?

Accepted Answer

It reduces skewness and compresses large values

Answer

A log transform compresses the long right tail, reducing skewness and lessening the influence of extreme values.

Data Science Practice Test