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

Binning, also known as discretization, is the process of transforming continuous numerical variables into discrete categorical intervals or 'bins'. This can help models capture non-linear relationships. [8, 22, 24]

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

One-hot encoding is used for nominal categorical variables where no intrinsic order exists. It creates new binary columns for each category to avoid implying a ranked relationship (e.g., Premium > Standard > Basic), which label encoding might suggest to the model. [20, 21, 27, 34]

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

A primary advantage of L1 regularization (Lasso) is its ability to produce sparse models. It adds a penalty equal to the absolute value of the magnitude of coefficients, which can force the coefficients of less important features to become exactly zero, effectively removing them from the model. [2, 11, 28, 29]

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

Wrapper methods use a specific machine learning algorithm to evaluate the quality of a subset of features. They train and test the model with different feature subsets (e.g., through forward selection or backward elimination) to find the optimal combination, which makes them computationally intensive but often leads to better performance for the chosen model. [16, 17, 18]

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

Principal Component Analysis (PCA) is a feature extraction technique used for dimensionality reduction. It transforms a set of correlated variables into a smaller set of new, uncorrelated variables called principal components, which are ordered by the amount of original variance they capture. [5, 15, 32, 33]

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE - Data Science Feature Engineering and Selection Questions and Answers