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K-means clustering is best suited for numeric data, as it relies on calculating the Euclidean distance between data points, which is meaningful for numerical values.

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.

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.

A graph of the WSS (Within-Cluster Sum of Squares) versus the value of K helps determine the optimal number of clusters. The "elbow" point on the graph represents the value of K at which adding more clusters no longer significantly improves the WSS.

An itemset is a set of discrete variables (or items) that are linked or appear together in a transaction or dataset, commonly used in association rule mining to find relationships between items.

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.

The formula for Naïve Bayes is:

For the renewal class, we are given:

● P(Class = Renewal) = 0.8 (since 80% renew the policy)

● P(Age > 65 years | Renewal) = 0.3

● P(Housing = Own | Renewal) = 0.9

● P(Renewal Month = August | Renewal) = 0.1

P(Renewal) = P(Renewal) × P(Age > 65 years | Renewal) × P(Housing = Own | Renewal) × P(Renewal Month = August | Renewal)

P(Renewal) = 0.8 × 0.3 × 0.9 × 0.1 = 0.0216

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 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.

MAD in MADlib stands for Multiple Access Design, which is a library for scalable in-database analytics, including machine learning and statistical methods.

An Excel file is an example of quasi-structured data because it contains structured data (like rows and columns) but may have irregularities, such as varying data formats, missing headers, or mixed data types within cells, making it less strictly structured than a relational database.

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.

Analysts value clarity and interpretability in data presentations. Clean, simple-to-understand visuals help them accurately assess the data, model outputs, and insights without unnecessary complexity.

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 rug() function in R adds small tick marks along the axes of a plot to represent the distribution of a variable, effectively showing it along both axes for better visual interpretation.