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

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.

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.

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.