The phrase "automatic model selection" is correct and usable in written English.
It can be used in contexts related to data science, machine learning, or statistical modeling where models are chosen automatically based on certain criteria or algorithms.
Example: "The software utilizes automatic model selection to determine the best predictive model for the given dataset."
Alternatives: "automated model choice" or "automatic model picking".
After the results of each permutation test were automatically collated, the DAG triggered automatic model selection and QTL processing, again performed using R/qtl (Broman et al. 2003).
Similarly after automatic model selection only FIGO stage was kept in the final model (selected in 94% bootstrap samples) and addition of MGB-2 to the model delivered no significant improvement (Table 4).
A synthesis of the authors' groundbreaking econometric research on automatic model selection, which uses powerful computational algorithms and theory evaluation.
In this book, leading econometricians David Hendry and Jurgen Doornik report on their several decades of innovative research on automatic model selection.
The minimum descriptive length and the leave-one-out error criteria are employed for automatic model selection.
A nested uniform design (UD) methodology is proposed for efficient, robust and automatic model selection for support vector machines (SVMs).
For forecasting each coefficient in our FDA model, and for constructing prediction intervals, we use exponential smoothing state space models [ 9] via the automatic model-selection algorithm of Hyndman et al. [ 10].
Historically, multiple methods are proposed for how to select the rank r, such as AIC-type (AIC, Akaike Information Criterion and MDL, Minimum Description Length [18]) which are based on information theoretic criteria or ESPRIT-type (ESTER, ESTimation Error [19] and SAMOS, Subspace-based Automatic Model Order Selection [20]) based on the shift invariance equation.
Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection.
Following the log data transformation, an automatic search procedure for model selection based on the AIC criterion was performed.
From the third-order linear regression model for the 26 factorial design, an automatic search procedure for model selection based on the AIC criterion resulted in the model described in Table 12.
