Quiz Part 2: Feature Engineering for Powerful Models

Question 1:

Answer: b) It transforms raw data into meaningful inputs that improve model performance.

Question 2:

Answer: d) Label encoding

(Label encoding is used for categorical feature transformation, not handling missing data.)

Question 3:

Answer: b) K-Nearest Neighbors (KNN) imputation

Question 4:

Answer: c) Logarithmic transformation

Question 5:

Answer: a) Min-Max Scaling adjusts values to a fixed range, while Standardization centers the data around a mean of zero and standard deviation of one.

Question 6:

Answer: c) It may create a very large number of columns, leading to high dimensionality.

Question 7:

Answer: c) Target Encoding

Question 8:

Answer: b) To capture the combined effect of multiple features on the target variable.

Question 9:

Answer: b) The features may cause multicollinearity and overfitting.

Question 10:

Answer: b) Polynomial feature creation

Bonus Question:

Answer: a) Recursive Feature Elimination (RFE)

Question 1:

Answer: b) It transforms raw data into meaningful inputs that improve model performance.

Question 2:

Answer: d) Label encoding

(Label encoding is used for categorical feature transformation, not handling missing data.)

Question 3:

Answer: b) K-Nearest Neighbors (KNN) imputation

Question 4:

Answer: c) Logarithmic transformation

Question 5:

Answer: a) Min-Max Scaling adjusts values to a fixed range, while Standardization centers the data around a mean of zero and standard deviation of one.

Question 6:

Answer: c) It may create a very large number of columns, leading to high dimensionality.

Question 7:

Answer: c) Target Encoding

Question 8:

Answer: b) To capture the combined effect of multiple features on the target variable.

Question 9:

Answer: b) The features may cause multicollinearity and overfitting.

Question 10:

Answer: b) Polynomial feature creation

Bonus Question:

Answer: a) Recursive Feature Elimination (RFE)

Question 1:

Answer: b) It transforms raw data into meaningful inputs that improve model performance.

Question 2:

Answer: d) Label encoding

(Label encoding is used for categorical feature transformation, not handling missing data.)

Question 3:

Answer: b) K-Nearest Neighbors (KNN) imputation

Question 4:

Answer: c) Logarithmic transformation

Question 5:

Answer: a) Min-Max Scaling adjusts values to a fixed range, while Standardization centers the data around a mean of zero and standard deviation of one.

Question 6:

Answer: c) It may create a very large number of columns, leading to high dimensionality.

Question 7:

Answer: c) Target Encoding

Question 8:

Answer: b) To capture the combined effect of multiple features on the target variable.

Question 9:

Answer: b) The features may cause multicollinearity and overfitting.

Question 10:

Answer: b) Polynomial feature creation

Bonus Question:

Answer: a) Recursive Feature Elimination (RFE)

Question 1:

Answer: b) It transforms raw data into meaningful inputs that improve model performance.

Question 2:

Answer: d) Label encoding

(Label encoding is used for categorical feature transformation, not handling missing data.)

Question 3:

Answer: b) K-Nearest Neighbors (KNN) imputation

Question 4:

Answer: c) Logarithmic transformation

Question 5:

Answer: a) Min-Max Scaling adjusts values to a fixed range, while Standardization centers the data around a mean of zero and standard deviation of one.

Question 6:

Answer: c) It may create a very large number of columns, leading to high dimensionality.

Question 7:

Answer: c) Target Encoding

Question 8:

Answer: b) To capture the combined effect of multiple features on the target variable.

Question 9:

Answer: b) The features may cause multicollinearity and overfitting.

Question 10:

Answer: b) Polynomial feature creation

Bonus Question:

Answer: a) Recursive Feature Elimination (RFE)