In short, since your main task is to select a learning algorithm and train it on some data, the two things that can go wrong are “bad algorithm” and “bad data.” Let’s start with examples of bad data.

  1. Insufficient Quantity of Training Data
  2. Non-representative Training Data
  3. Poor-Quality Data
  4. Irrelevant Features

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