Pass the EMC Data Science D-DS-FN-23 Questions and answers with ValidTests

The decision tree having 17 layers and a leaf node with only three customers suggests that the tree is overfitting the data. This is because a deep tree with many layers can lead to overfitting, where the model fits the training data too closely, capturing noise and small details that do not generalize well to new data. Reducing the tree depth or pruning the tree can help mitigate overfitting.

Defining categories such as performance levels falls under the model planning phase, where data is prepared and structured for analysis. This step involves selecting techniques and identifying how data will be used in modeling.

Regular expressions are highly useful for in-database text analysis, where they can be used to search, match, and manipulate text data efficiently within databases, especially for tasks like pattern matching, data extraction, and validation.

In linear regression, the model output includes coefficients that indicate the relative impact of the input variables on the outcome. These coefficients represent how much each input variable influences the predicted outcome.

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.

The primary role of a business intelligence analyst on an analytics project is to provide business-domain expertise. They bridge the gap between the technical aspects of the project and the business requirements, ensuring that the data and analysis align with the business objectives.

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.

In a user-defined aggregate function (UDAF), FFUNC refers to the optional final calculation function. It is used to perform any final calculation or transformation on the aggregated result before it is returned to the user.

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.

In logistic regression, the threshold determines the cutoff for classifying an outcome. If the probability exceeds the threshold (0.3 in this case), the model predicts the positive class—here, that the borrower will default.