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After estimating over a grid of λ to calculate the LASSO path, this criterion finds the value of λ that minimizes the deviance of the complement of subsample k, i.e., where is the set of individuals not selected for subsample k, and is the predicted probability of based upon applied to the design matrix of the complement subsample.
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Models were selected based on minimizing the deviance information criteria (DIC).
Consequently, model selection can proceed by minimizing the deviance,, where is the maximum likelihood estimate (MLE) of θ.
For large sample sizes, the model minimizing the deviance is the model with the highest posterior probability (see, for example, [ 20]).
By minimizing the deviance between observed and modeled breast cancer incidence, screen detection, and interval cancer rates, the optimal model parameters were chosen.
The constraint was expressed in terms of a penalty parameter; the optimal value of this parameter was determined by minimizing the deviance of the multivariate logistic model (hereafter called LASSO-max) averaged over five-fold cross-validation subsamples [ 31].
However, we performed two quality controls over the selection of adjacent normal tissues to minimize the deviances.
Both models are trained with the same target variable: silver_attain, and try minimize the binomial deviance (Log Loss) of prediction error.
These seasonal ranges were chosen to minimize the residual deviance of the regression.
The maximum likelihood estimates of b and tm were obtained by minimizing the binomial deviance of the model from the observed data.
We optimised ε and cN by fitting to the incidence of IPD in each age group from the US data by minimizing the Poisson deviance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com