Question

Miyato et. al. introduced the idea of virtual adversarial examples to reduce this phenomenon. Violations of the one-in-ten rule leads to higher prevalence of this phenomenon. Michael A. Babyak’s paper “What You See May Not Be What You Get” is a “nontechnical” introduction to this phenomenon, which unintuitively occurs only at intermediate size in double descent. The 2k term in the Akaike (15[1])information criterion adjusts for this phenomenon. (15[1])Elastic net reduces this phenomenon by combining the (*) L1 and L2 (10[1])norm penalties from LASSO and ridge regression. Early stopping (10[1])is an example of regularization (-5[1])techniques used to mitigate this phenomenon, which is exhibited by models with very low bias and very high variance. (-5[1])For 10 points, name this phenomenon where a model has too many parameters, causing test error to be much larger than training error. ■END■ (10[2])

ANSWER: overfitting [prompt on generalization error; prompt on bias–variance tradeoff before “variance”; prompt on answers describing a high number of variables/parameters or high complexity with “which causes what phenomenon?”]
<Science - Other Science - Math>
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Buzzes

PlayerTeamOpponentBuzz PositionValue
Jaimie CarlsonBanned from ARGOS"Powers a question on Stancyzk" that's a clown question bro6115
Natan Holtzmanthrow away your cards, rally in the streetsAw we're so sorry to hear that maman died today, she gets five big booms6715
Aditya GangradePahkin' the Ahgo|madam|7810
Steven YuanCLEVELAND, THIS IS FOR YOU!I wish it were possible to freeze time so I would never have to watch you retire8710
Walter ZhangImport PandasHu up Jinning they Tao92-5
John ChenUBCThompson et al.111-5
Jonathan SchnipperHu up Jinning they TaoImport Pandas13510
Joel MilesThompson et al.UBC13510

Summary

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