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Real World Active Learning

Machine learning isn't a set-it-and-forget-it operation. Even with solid examples, ML algorithms can still fail and end up blocking important emails, filtering out useful content, and causing a variety of other problems.

In this report, industry analyst Ted Cuzzillo examines real-world examples of active learning and you'll discover, the point at which algorithms fail is precisely where there's an opportunity to insert human judgment to actively improve the algorithm's performance.

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Report Insights

Help algorithms decrease the uncertainty of their results in email spam filtering and online search accuracy
Effectively use crowds on CrowdFlower in your ML training project, without incurring undue costs

Select training data from areas where the data volume is greatest
Use multiple classification methods to better train ML algorithms