Sentence examples for more predictor variables from inspiring English sources

Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model.

Multicollinearity exists where there are strong correlations between two or more predictor variables.

Adding more predictor variables have proven to be helpful in increasing model accuracy.

Logistic regression measures the relationship between a binary response variable (Disease-gene network) and one or more predictor variables (drug-gene networks).

In statistics, multicollinearity is a situation in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree of accuracy.3Both the Variance Inflation Factor (VIF) method and Correlation Coefficient Table are commonly used for the test, as well in this paper.

Calculations of model prediction error on the training data indicated little or no improvement in model performance when more predictor variables are included, i.e. models with all predictor variables (full), or built stepwise (step).

With this approach, they eliminated the problem of having more predictor (explanatory/independent) variables than regressands (response/dependent variables), but it does not fully take advantage of the whole marker information because only markers with a significant effect are included in the final model.

Basic human characteristics such as beliefs, attitudes and the angler's consumptive orientation were more meaningful predictor variables than typical angler variables such as demographics, angler experience or angler preferences (e.g. species preference).

The objective is to model a response variable in terms of one or more independent predictor variables.

Model compares Bayesian ZIP and classical ZIP approaches were identified more significant predictor variables, numerical value different in standard error and other techniques.

When two (or more) continuous predictor variables are dichotomized, the resulting 2 × 2 analyses are not necessarily conservative.