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All Blog Posts Tagged 'bias' (5)

Omitted Variables in Linear Regressions

The importance of completeness of linear regressions is an often-discussed issue. By leaving out relevant variables the coefficients might be inconsistent.

But why on earth?! 

Assuming a linear complete model of the form:

z = a + bx + cy + ε.

Where z is supposed to be dependent, x and y are independent and ε is the error term.

Now we drop y to check…

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Added by Frank Raulf on November 13, 2019 at 2:00am — No Comments

Detecting Bias with SHAP

StackOverflow’s annual developer survey concluded earlier this year, and they have graciously published the (anonymized) 2019 results for analysis. They’re a rich view into the experience of software developers around the world — what’s their favorite editor? how many years of experience? tabs or spaces? and crucially, salary. Software engineers’ salaries are good, and sometimes both eye-watering and news-worthy.

The tech industry is also painfully aware that it does not always live…

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Added by Sean Owen on August 8, 2019 at 8:00am — No Comments

Is Model Bias a Threat to Equal and Fair Treatment? Maybe, Maybe Not.

Summary:  There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail.  It’s not as simple as it seems and here we try to take a more nuanced look.  The result is not as threatening as many headlines make it seem.

 …

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Added by William Vorhies on June 5, 2018 at 8:00am — No Comments

7 Common Biases That Skew Big Data Results

Summary:  Flawed data analysis leads to faulty conclusions and bad business outcomes. Beware of these seven types of bias that commonly challenge organizations' ability to make smart decisions.

This is a great article by Lisa Morgan originally published on InformationWeek.com.  See the original article…

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Added by William Vorhies on July 16, 2015 at 2:50pm — 2 Comments

Fooled by Twitter Data

Data scientists must always remember that data sets are not objective -  they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting,…

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Added by Michael Walker on October 7, 2013 at 9:14pm — No Comments

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