MS-DS Master of Data science Feature Engineering

Question 1

Which technique is used to reduce the impact of outliers when scaling numerical features?

Accepted Answer

Robust Scaling

Answer

Robust Scaling uses the median and interquartile range (IQR) instead of mean and standard deviation, making it resistant to the influence of outliers.

Question 2

What is the primary purpose of one-hot encoding in feature engineering?

Accepted Answer

Convert nominal categorical variables into binary indicator columns

Answer

One-hot encoding converts nominal categorical variables into binary (0/1) indicator columns so that machine learning algorithms can process them without implying an ordinal relationship.

Question 3

Label encoding is most appropriate for which type of categorical variable?

Accepted Answer

Ordinal variables with a meaningful rank order

Answer

Label encoding assigns integer values that preserve rank order, making it appropriate for ordinal variables where the numerical order is meaningful (e.g., low=1, medium=2, high=3).

Question 4

Which feature transformation is most commonly applied to correct a right-skewed (positively skewed) distribution?

Accepted Answer

Applying a log transformation

Answer

A log transformation compresses large values more than small values, which reduces right skew and helps bring the distribution closer to normal.

Question 5

What does 'feature interaction' refer to in the context of feature engineering?

Accepted Answer

Creating new features by combining two or more existing features

Answer

Feature interaction involves creating new features by combining existing ones (e.g., multiplication or ratio), which can capture relationships that individual features cannot represent.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Feature Engineering