Sentence examples for given predictors from inspiring English sources
A PM produces a predicted conditional probability of success given predictors, but in a model-agnostic data-driven fashion.
It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios.
It is widely used both as an association model and a predictive model, to look at (a) the conditional probability of outcome, given predictors, and (b) predictor effect size estimates using conditional odds ratios.
The probability of a gene belonging to a class given predictors X is computed under the assumption that predictors are conditionally independent of one another given a class label.
The idea is that, starting from binary (0/1) outcomes for each subject, a PM generates an estimated expected value for each subject, which is just the conditional probability of success for that subject given predictors.
For a continuous response (y i; i = 1, …, n) the data equation is represented as y i = η i + ε i, where η i is a linear predictor (the expected value of y i given predictors) and ε i are independent normal model residuals with mean zero and variance w i 2 σ ε 2. Here, the w i ′ s are user-defined weights (by default BGLR sets w i = 1 for all data-points) and σ ε 2 is a residual variance parameter.
The plots shows how a given predictor is related to the response with all other predictors held constant.
Reading the sentence one way yields a truth: against any given avoider, Predictor can finish and against any given predictor, Avoider can finish.
If missingness for a given predictor was associated with both the predictor and the outcome (re-incarceration), this could bias our results.
Wrapper method assesses a subset of features according to their usefulness to a given predictor, which is better in this scenario.
The amount of variance explained by a given predictor variable is the square of the correlation between that variable and the behavior in question.
