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reciprocally    
ad. 相互地;相反地

相互地;相反地

reciprocally
adv 1: (often followed by `for') in exchange or in
reciprocation; "gave up our seats on the plane and in
return received several hundred dollars and seats on the
next plane out"; "we get many benefits in return for our
taxes" [synonym: {in return}, {reciprocally}]
2: in a mutual or shared manner; "the agreement was mutually
satisfactory"; "the goals of the negotiators were not
reciprocally exclusive" [synonym: {mutually}, {reciprocally}]
3: in an inverse or contrary manner; "inversely related";
"wavelength and frequency are, of course, related
reciprocally"- F.A.Geldard [synonym: {inversely}, {reciprocally}]


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  • Is there any reason to prefer the AIC or BIC over the other?
    AIC should rarely be used, as it is really only valid asymptotically It is almost always better to use AICc (AIC with a correction for finite sample size) AIC tends to overparameterize: that problem is greatly lessened with AICc The main exception to using AICc is when the underlying distributions are heavily leptokurtic
  • How to compare models on the basis of AIC? - Cross Validated
    AIC tries to select a model (among the examined ones) that most adequately describes reality (in the form
  • What is a difference between a Low AIC and a Bigger AIC
    AIC is the Akaike information criterion In general, a model with smaller AIC is a better model As an alternative, people also use BIC to choose model For Bayesian models, DIC is also very popular In modern statistics, we often use cross validation to choose models instead of AIC
  • What selection criteria to use and why? (AIC, RMSE, MAPE . . . - Cross . . .
    AIC based weights are popular for prediction, because of the link between these and maximum likelihood estimation For a very enthusiastic view of this type of approach, you could refer to Burnham and Anderson's "Model selection and multimodel inference"
  • r - AIC guidelines in model selection - Cross Validated
    AIC and BIC hold the same interpretation in terms of model comparison That is, the larger difference in either AIC or BIC indicates stronger evidence for one model over the other (the lower the better) It's just the the AIC doesn't penalize the number of parameters as strongly as BIC
  • logistic - What is the difference in what AIC and c-statistic (AUC . . .
    AIC is telling you how good your model fits for a specific mis-classification cost AUC is telling you how good your model would work, on average, across all mis-classification costs When you calculate the AIC you treat your logistic giving a prediction of say 0 9 to be a prediction of 1 (i e more likely 1 than 0), however it need not be
  • GAM (mgcv): AIC vs Deviance Explained - Cross Validated
    This inquiry began with a reviewer insisting that AIC was penalized and deviance is not, and this seems to agree with gam() help which indicates model aic is and deviance is not penalized Howerver, the results above support your comment, and or that the aic penalty is fairly negligible here
  • How does AIC vs. LASSO work? - Cross Validated
    So let's make an assumption of normality (so that MLE == OLS) and take a look at the AIC equation from wiki: AIC = 2k + n ln(RSS) here k is the number of parameters (variables), n is the sample size, and RSS is the residual sum of squares So for a given k and n we minimize the AIC by simply fitting for standard ols coefficeints
  • 英伟达官方推荐显卡品牌名单曝光，昂达被剔除，盈通翔升上榜
    铭瑄、梅捷这两个是商科集团的，铭瑄虽然不是aic，但它可以算的上预备aic，英伟达是可以给它协调芯片的，这要比盈通翔升这些又要高点了。 它的芯片也是正规渠道来的，NV官网也直接把铭瑄的4070ti挂着卖了，这是除了AIC以为唯一一个非AIC品牌上架NV官网的。





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