13 Ways Machine Learning Can Steer You Wrong - InformationWeek

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8/17/2016
07:06 AM
Lisa Morgan
Lisa Morgan
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13 Ways Machine Learning Can Steer You Wrong

Applying machine learning and artificial intelligence to your decision-making can help your business stay competitive. But a lot can go wrong along the way. Without the proper checks and balances, machine learning efforts can spiral out of control, exposing your organization to risks. Here are 13 pitfalls to avoid.
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Inaccurate Predictions 

Machine learning is often used to make predictions. Examples include improving search results, anticipating movie or product selections, anticipating customer purchasing behavior, or predicting new types of hacking techniques. One reason predictions may be inaccurate may have something to do with 'overfitting,' which occurs when a machine learning algorithm adapts itself too much to the noise in data, rather than uncovering the underlying signal.
'If you try to fit an extremely complex model to a small amount of data, you can always force it to [fit], but it won't generalize well to future data,' said Spencer Greenberg of ClearerThinking.org, in an interview. 'Essentially, your complex model will try too hard to hit every data point exactly, including random fluctuations that should be ignored, rather than modeling the gist of the data. The complexity of the model you are fitting must be selected based on the amount of data you have, and how noisy it is.'
(Image: stux via Pixabay)

Inaccurate Predictions

Machine learning is often used to make predictions. Examples include improving search results, anticipating movie or product selections, anticipating customer purchasing behavior, or predicting new types of hacking techniques. One reason predictions may be inaccurate may have something to do with "overfitting," which occurs when a machine learning algorithm adapts itself too much to the noise in data, rather than uncovering the underlying signal.

"If you try to fit an extremely complex model to a small amount of data, you can always force it to [fit], but it won't generalize well to future data," said Spencer Greenberg of ClearerThinking.org, in an interview. "Essentially, your complex model will try too hard to hit every data point exactly, including random fluctuations that should be ignored, rather than modeling the gist of the data. The complexity of the model you are fitting must be selected based on the amount of data you have, and how noisy it is."

(Image: stux via Pixabay)

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LisaMorgan
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LisaMorgan,
User Rank: Moderator
8/18/2016 | 11:59:11 AM
Re: It is cheaper to do ML wrong than it is to do it right
I think there has to be greatest overall awareness of what it takes to do machine learning right, what can happen if you don't do it right, and that input (including investment) is going to determine output (including ROI).
StevenL530
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StevenL530,
User Rank: Strategist
8/18/2016 | 10:17:55 AM
It is cheaper to do ML wrong than it is to do it right
The common thread I am seeing in each of the 13 is that avoiding bad ML results is going to require a greater investment in time, people, or follow-up to ensure that the outcomes of applying ML remain positive. Too many of my clients have a habit of valuing low cost over high quality.

One of the critical steps in QA for any new system or feature is the comparison of actual results to expected results. If the corporate objectives of ML is to find the unexpected insight, how will incorrect unexpected ML outcomes be sifted out from the set of all possible unexpected outcomes?
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