Chapter 1. Data Understanding

In this chapter, we will cover:

• Using an empty aggregate to evaluate sample size
• Evaluating the need to sample from the initial data
• Using CHAID stumps when interviewing an SME
• Using a single cluster K-means as an alternative to anomaly detection
• Using an @NULL multiple Derive to explore missing data
• Creating an Outliers report to give to SMEs
• Detecting potential model instability early using the Partition node and Feature Selection node