D-DS-FN-23 Dell Data Science Foundations Question and Answers

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.

When building a K-means clustering model, it's important to ensure that the data is properly scaled, as K-means is sensitive to the scale of the variables. If the variables have different scales, the clustering may not segment as expected. Standardizing or normalizing the data can often improve the results.

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 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.

A box and whisker plot is ideal for visualizing the relationship between time spent and product category, especially when you have multiple categories. It shows the distribution of time spent in each product category, including the median, quartiles, and any potential outliers.

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.

The R-squared value is approximately 0.96, indicating that 96% of the variance in the data is explained by the model.

Recalibration of the model should be assessed during the Operationalize phase, as this is when the model is deployed and monitored in real-world conditions, making it easier to identify if it requires adjustments based on its performance.

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

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).

The naïve Bayes conditional independence assumption is applied to simplify the probability calculations by assuming that the features are independent given the class label. This reduces the complexity of the model and makes computation feasible even with many features.

A logarithmic transformation is commonly used to reduce right skewness (long tail to the right) and make data more normally distributed.

A key business driver for Big Data analytics adoption is the ability to identify new business opportunities by analyzing large and complex datasets, which can provide valuable insights for decision-making and strategy development.