Sentence examples for many predictor models from inspiring English sources

Because many predictor models were generated (43 KNNxVal and 4 KNN models), we identified the 37 most predictive genes as those that were used in at least two of the KNNxVal models and were used in at least one of the KNN models.

However, if one includes too many predictors, the model is prone to imitating random properties of the data set, which are not present in the underlying sampling distribution of the data.

The ROC AUC and R of the different models presented in table 2 indicate that the accuracy of the predicted BASDAI50 response and predicted partial remission was similar when models with many predictor variables were compared with models with few variables.

Another well-known source of generalization loss is the use of too many predictor variables in the model [ 33, 34, 39, 41, 46]: the greater the number of predictor variables, the greater the number of model parameters to be estimated.

The statistical models include many predictor variables: nine on the patient level and nine on the practice level.

This confusion is due to correlations between, and among the many predictors included in models.

Third, our models include rather many predictor variables, which may increase the risk for overfitting.

Also cross-validation allows for a fairer comparison of operating characteristics across prediction rules involving different numbers of predictors, appropriately penalizing for "overfitting" the data when including too many predictors in the model.

Many predictor variables were significantly correlated with each other.

We use the concept of explained variation and area under the Receiver Operating Characteristic (ROC) curve for developing models and in determining how many predictors to include in final model.

Placing a penalty on the size of coefficients in the multiple regression leads to moderated estimates of coefficient effects, allowing their stable estimation even when many predictors are in the model.