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Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Total assessment cost with different cost models

In my previous post, I found that relevance and uncertainty selection needed similar numbers of document relevance assessments to achieve a given level of recall. I summarized this by saying the two methods had similar cost. The number of documents assessed, however, is only a very approximate measure of the cost of a review process, […] → Read More: Total assessment cost with different cost models

Total review cost of training selection methods

My previous post described in some detail the conditions of finite population annotation that apply to e-discovery. To summarize, what we care about (or at least should care about) is not maximizing classifier accuracy in itself, but minimizing the total cost of achieving a target level of recall. The predominant cost in the review stage […] → Read More: Total review cost of training selection methods

Total review cost of training selection methods

My previous post described in some detail the conditions of finite population annotation that apply to e-discovery. To summarize, what we care about (or at least should care about) is not maximizing classifier accuracy in itself, but minimizing the total cost of achieving a target level of recall. The predominant cost in the review stage […] → Read More: Total review cost of training selection methods

Total review cost of training selection methods

My previous post described in some detail the conditions of finite population annotation that apply to e-discovery. To summarize, what we care about (or at least should care about) is not maximizing classifier accuracy in itself, but minimizing the total cost of achieving a target level of recall. The predominant cost in the review stage […] → Read More: Total review cost of training selection methods

Total review cost of training selection methods

My previous post described in some detail the conditions of finite population annotation that apply to e-discovery. To summarize, what we care about (or at least should care about) is not maximizing classifier accuracy in itself, but minimizing the total cost of achieving a target level of recall. The predominant cost in the review stage […] → Read More: Total review cost of training selection methods

Finite population protocols and selection training methods

In a previous post, I compared three methods of selecting training examples for predictive coding—random, uncertainty and relevance. The methods were compared on their efficiency in improving the accuracy of a text classifier; that is, the number of training documents required to achieve a certain level of accuracy (or, conversely, the level of accuracy achieved […] → Read More: Finite population protocols and selection training methods

Finite population protocols and selection training methods

In a previous post, I compared three methods of selecting training examples for predictive coding—random, uncertainty and relevance. The methods were compared on their efficiency in improving the accuracy of a text classifier; that is, the number of training documents required to achieve a certain level of accuracy (or, conversely, the level of accuracy achieved […] → Read More: Finite population protocols and selection training methods

Finite population protocols and selection training methods

In a previous post, I compared three methods of selecting training examples for predictive coding—random, uncertainty and relevance. The methods were compared on their efficiency in improving the accuracy of a text classifier; that is, the number of training documents required to achieve a certain level of accuracy (or, conversely, the level of accuracy achieved […] → Read More: Finite population protocols and selection training methods

Finite population protocols and selection training methods

In a previous post, I compared three methods of selecting training examples for predictive coding—random, uncertainty and relevance. The methods were compared on their efficiency in improving the accuracy of a text classifier; that is, the number of training documents required to achieve a certain level of accuracy (or, conversely, the level of accuracy achieved […] → Read More: Finite population protocols and selection training methods