3 Ways to Test the Accuracy of Your Predictive Models

In data mining, data scientists use algorithms to identify previously unrecognized patterns and trends hidden within vast amounts of structured and unstructured information. These patterns are used to create predictive models that try to forecast future behavior.
These models have many practical business applications—they help banks decide which customers to approve for loans, and marketers use them to determine which leads to target with campaigns.
But extracting real meaning from data can be challenging. Bad data, flawed processes and the misinterpretation of results can yield false positives and negatives, which can lead to inaccurate conclusions and ill-advised business decisions.

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