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In practice, λ is chosen so as to optimize the goodness of fit of the model.
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Multivariate models were then built in a forward manner to optimize the overall goodness-of-fit of the model, by maximizing R adjusted on the number of covariates.
Hence, in a given set of models the model with the minimum AIC optimizes the trade-off between the goodness-of-fit and the model complexity.
For the co-morbidities, a bootstrap stepwise procedure that assigned an importance rank for the predictors in the logistic regression was implemented to identify the set of conditions that significantly predicted the risk of the outcome and optimized the trade-off between the goodness-of-fit of the final model and parsimony.
Now, in order to get optimized model, we use Akaike Information Criteria (AIC), which aims to optimize the exchange between goodness of fit and model parsimony.
For each dataset from both collections of toy datasets and real-life microarray datasets, we have shown that there exists a value of the DDP which optimizes several characteristics representing the goodness of the predictor set.
The goodness-of-fit was based on the adjusted R2.
The goodness-of-fit (adjusted R2) was 0.08.
The goodness-of-fit was calculated by using the Hosmer-Lemeshow goodness-of-fit test.
This is carried out by optimizing a cost function that measures the goodness of this fit.
The aim is to find the parameters that give the best (optimal) fit to a set of experimental data, which entails minimizing (optimizing) a cost function that measures the goodness of this fit.
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