For example, in the case of categorical predictor variables, predictor variables with many categories are favored over predictor variables with few categories.
With 61 "events", 4 robust true predictors (15 observations per predictor variable) of non-responders can be identified and used for the risk score of non-responders [ 30].
This provided 1 334 observations over the 29-year period.
In contrast, suppose observations of the predictor variable are IID but observations of the target variable are not well described by the tree.
where θ i is the i th observation for the dependent variable θ (i = 1, 2, …16), ψ i is the i th observation for the predictor |ψ|.
The use of this observation as a predictor of host specificity awaits experimental confirmation.
In observations, challenge over checklist use was very rarely witnessed.
The predictor avoids over-fitting the predictor to a specific sequence, i.e in deterministic settings, a predictor that is applicable to different sets of sequences [20,37].
CART also has algorithms that account for missing data amongst the predictors; whereas with logistic regression, entire observations are eliminated if a single predictor is missing.
For fatigue and HFS, the observed severity scores (last observation carried forward) were evaluated as predictors of OS by including each observed score as a predictor.
