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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