Machine Learning Times
Machine Learning Times
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7 years ago
Top 10 Analytic Mistakes–Today #0: Lacking Relevant Data

 Mining data to extract useful and enduring patterns remains a skill arguably more art than science. Pressure enhances the appeal of early apparent results, but it is all too easy to fool oneself. How can one resist the siren songs of the data and maintain an analysis discipline that will lead to robust results? It is essential to not: lack (proper) data, focus on training, rely on one technique, ask the wrong question, listen (only) to the data, accept leaks from the future, discount pesky cases, extrapolate (practically and theoretically), answer every inquiry, sample casually, or believe the best model.

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