DSE - Data Science Feature Engineering and Selection Questions and Answers

Question 1

A data scientist is working with a dataset that includes a 'temperature' feature with continuous values.
To improve model performance, they decide to convert this feature into categories like 'Cold', 'Warm', and 'Hot'.
Which feature engineering technique is being applied?

Accepted Answer

Binning

Answer

Normalization

Answer

One-Hot Encoding

Answer

Principal Component Analysis

Question 2

In a dataset for predicting customer churn, a categorical feature 'Subscription_Tier' has three levels: 'Basic', 'Standard', and 'Premium'. A data scientist uses one-hot encoding on this feature. What is the primary reason for choosing one-hot encoding over label encoding in this scenario?

Accepted Answer

To prevent the model from assuming a false ordinal relationship between the categories.

Answer

To reduce the dimensionality of the dataset.

Answer

To ensure the model interprets the feature as ordinal, with a natural ranking.

Answer

To handle missing values within the feature.

Question 3

A machine learning engineer is using L1 regularization (Lasso) for a linear regression model with a large number of features. What is a key benefit of using L1 regularization in this context?

Accepted Answer

It can perform automatic feature selection by shrinking some feature coefficients to exactly zero.

Answer

It guarantees a more complex and flexible model.

Answer

It is effective at handling multicollinearity by shrinking correlated coefficients together.

Answer

It ensures that all original features are retained in the final model.

Question 4

Which of the following feature selection methods is characterized by its use of a predictive model to evaluate the usefulness of a feature subset, but is computationally expensive due to its iterative nature?

Accepted Answer

Wrapper methods

Answer

Filter methods

Answer

Embedded methods

Answer

Dimensionality reduction

Question 5

A data scientist is tasked with reducing the dimensionality of a dataset with 100 correlated features while retaining as much of the original variance as possible. The goal is to create a new, smaller set of uncorrelated features. Which technique is most appropriate for this task?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Variance Thresholding

Answer

Recursive Feature Elimination

Answer

Mean/Median Imputation

Question 6

When dealing with missing data in a numerical feature, a data scientist decides to replace the missing entries with the feature's median value. This approach is most appropriate under which of the following circumstances?

Accepted Answer

The goal is to remove all rows containing any missing values.

Answer

The feature has a large number of unique categories.

Answer

The missing data is classified as 'Missing Not At Random' (MNAR).

Answer

The numerical feature's distribution is significantly skewed by outliers.

A data scientist is working with a dataset that includes a 'temperature' feature with continuous values.To improve model performance, they decide to convert this feature into categories like 'Cold', 'Warm', and 'Hot'.Which feature engineering technique is being applied?

A data scientist is working with a dataset that includes a 'temperature' feature with continuous values.
To improve model performance, they decide to convert this feature into categories like 'Cold', 'Warm', and 'Hot'.
Which feature engineering technique is being applied?