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

Multinomial logistic regression should be used when the dependent variable has more than two categories (i.e., it is categorical with more than two possible outcomes). This differs from binary logistic regression, which is used when the dependent variable is binary (i.e., has two categories).

Data conditioning in the data preparation phase refers to the process of cleaning the data, normalizing datasets, and performing transformations to ensure the data is in a suitable format for analysis or modeling.

Naïve Bayes classifiers are particularly effective for handling high-dimensional data (many features) and are resistant to overfitting. Despite the strong independence assumption, they can still perform well in many real-world scenarios, especially with large datasets and many features.

The GROUPING extension in SQL OLAP is used to return a result for each output row, where 1 identifies a summary row and 0 identifies grouped rows. This helps distinguish between regular grouped rows and summary rows in the result set.

A Type I error occurs when the null hypothesis is rejected even though it is actually true. This is also known as a "false positive" in hypothesis testing.

Both matrix and array data structures in R can only contain one data type across all their elements, ensuring consistency in the structure.

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.

The GROUP BY clause in the SQL statement implies the use of an aggregate function. In this case, avg(prod_cost) is an aggregate function that calculates the average of the product costs for each group of products.