Rental properties took about six weeks and substantial engineering oversight to reach 90% precision and recall
这个时间框架显示了复杂税务处理任务的AI训练周期。90%的精确率和召回率对于复杂的租赁房产税务处理是一个很好的基准。需要'大量工程监督'表明即使是先进AI系统也需要人类专家的指导和监督，特别是在专业领域。
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- Last 7 days
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- May 2020
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notes.andymatuschak.org notes.andymatuschak.org
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Ericsson claims (2016, p. 98) that there is no deliberate practice possible for knowledge work because there are no objective criteria (so, poor feedback), because the skills aren’t clearly defined, and because techniques for focused skill improvement in these domains aren’t known.
According to Ericsson deliberate practice for knowledge work is not possible because the criteria are not objective (you don't know if you're doing well).
This collides with Dr. Sönke Ahrens' contention that note taking, specifically elaboration, instantiates two feedback loop. One feedback loop in that you can see whether you're capturing the essence of what you're trying to make a note on and a second feedback loop in that you can see whether your note is not only an accurate description of the original idea, but also a complete one.
Put differently, note taking instantiates two feedback loops. One for precision and one for recall.
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- May 2017
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www.analyticsvidhya.com www.analyticsvidhya.com
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Precision: It is a measure of correctness achieved in positive prediction i.e. of observations labeled as positive, how many are actually labeled positive. Precision = TP / (TP + FP) Recall: It is a measure of actual observations which are labeled (predicted) correctly i.e. how many observations of positive class are labeled correctly. It is also known as ‘Sensitivity’. Recall = TP / (TP + FN)
Example: In cancer research you may want higher recall, Since you want all actual positive observations to classified as True Positive. A lower Precision maybe alright because some healthy people classified as cancerous can be rectified later.
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