A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)

Machine learning algorithms require numerical input. Categorical features, which represent qualitative data (e.g., 'color', 'city'), must be transformed into a numerical format. One-hot encoding is a technique that converts a categorical feature into multiple new binary (0 or 1) columns, with each column representing a unique category. This is ideal for nominal data where no inherent order exists. Label encoding is another technique that assigns a unique integer to each category (e.g., 'low' -> 0, 'medium' -> 1, 'high' -> 2). This method is particularly suitable for ordinal data where a meaningful order is present. Both are fundamental techniques for making categorical features useful for model building.

A. Normalization: This technique scales numerical features to a specific range (e.g., 0 to 1) and is not used for categorical data.
C. Linearization: This is not a standard term for encoding categorical features; it relates to transforming data to fit a linear model.
E. Scaling: This is a general term for adjusting the range of numerical data, not for converting categorical data into a numerical format.
F. Pivoting: This is a data reshaping operation used for aggregation and summarization, not a feature encoding technique for modeling.

1. Scikit-learn Developers. (n.d.). 6.3. Preprocessing data. Scikit-learn User Guide. Retrieved from scikit-learn.org.

Section 6.3.4

"Encoding categorical features": This section explicitly details the use of OneHotEncoder to "create a binary column for each category" and OrdinalEncoder (which functions like label encoding for features) to "convert each category into an integer value." This directly supports the use of both one-hot and label encoding for categorical features.

2. VanderPlas

J. (2016). Python Data Science Handbook. O'Reilly Media

Inc.

Chapter 5

Section "Feature Engineering"

Subsection "Categorical Features": This section explains the problem with feeding categorical data directly to models and presents solutions. It states

"For categorical features... we can split the category into a number of binary features... This is often called one-hot encoding." It also discusses the alternative of using integer codes for categories (label encoding).

3. Massachusetts Institute of Technology. (2020). MIT 6.036: Introduction to Machine Learning

Fall 2020. MIT OpenCourseWare.

Lecture 9 Notes

Section 4

"Features": The course notes discuss feature representation

stating: "Discrete features with no natural ordering (like 'animal' = 'dog'

'cat'

'bird') are often converted into a one-hot vector representation." This validates one-hot encoding as a standard academic practice.