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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
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