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Lecture 33

Classification

DATA 8

Spring 2017

Slides created by John DeNero (denero@berkeley.edu) and Ani Adhikari (adhikari@berkeley.edu)

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Announcements

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Classification

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Classification Examples

(Demo)

Fraud: order fraud (Amazon, PayPal, etc.), account fraud (LinkedIn)

Dating: https://www.wired.com/2014/01/how-to-hack-okcupid/

If, through statistical sampling, McKinlay could ascertain which questions mattered to the kind of women he liked, he could construct a new profile that honestly answered those questions and ignored the rest. He could match every woman in LA who might be right for him, and none that weren’t.

Diagnosis: http://www.newyorker.com/magazine/2017/04/03/ai-versus-md

In June, 2015, Thrun’s team began to test what the machine had learned from the master set of images by presenting it with a “validation set”: some fourteen thousand images that had been diagnosed by dermatologists (although not necessarily by biopsy). Could the system correctly classify the images into three diagnostic categories—benign lesions, malignant lesions, and non-cancerous growths? The system got the answer right seventy-two per cent of the time. (The actual output of the algorithm is not “yes” or “no” but a probability that a given lesion belongs to a category of interest.) Two board-certified dermatologists who were tested alongside did worse: they got the answer correct sixty-six per cent of the time.

Personality: https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win (Cambridge Analytica—Board member Steve Bannon)

Link personality tests to Facebook profiles

"Followers of Lady Gaga were most probably extroverts, while those who "liked" philosophy tended to be introverts. While each piece of such information is too weak to produce a reliable prediction, when tens, hundreds, or thousands of individual data points are combined, the resulting predictions become really accurate."

"Up to now, explains Nix, election campaigns have been organized based on demographic concepts. "A really ridiculous idea. The idea that all women should receive the same message because of their gender—or all African Americans because of their race." What Nix meant is that while other campaigners so far have relied on demographics, Cambridge Analytica was using psychometrics."

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Attendance

bit.ly/data8here

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Classifiers

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Training a Classifier

Classifier

Attributes of an example

Predicted label of the example

Population

Labels

Sample

Training

Set

Test

Set

Model the association between attributes & labels

Estimate the accuracy of the classifier