D-DS-FN-23 Exam Dumps : Dell Data Science Foundations

The metrics used to calculate relevance in text analysis are TF (Term Frequency) and IDF (Inverse Document Frequency). TF measures how frequently a term appears in a document, while IDF adjusts the importance of the term based on how common it is across all documents. Their combination, known as TF-IDF, helps evaluate the relevance of terms in a given document relative to a larger corpus.

Feature selection involves identifying the most useful input variables that contribute meaningfully to the model, improving accuracy and reducing complexity during the model building phase.

The dependent variable in logistic regression is categorical, typically binary (e.g., success/failure, yes/no), since the goal is to model the probability of an event occurring.