Search results
Results from the WOW.Com Content Network
The effect(s) of such misclassification can vary from an overestimation to an underestimation of the true value. [4] Statisticians have developed methods to adjust for this type of bias, which may assist somewhat in compensating for this problem when known and when it is quantifiable. [5]
Recall bias is of particular concern in retrospective studies that use a case-control design to investigate the etiology of a disease or psychiatric condition. [ 3 ] [ 4 ] [ 5 ] For example, in studies of risk factors for breast cancer , women who have had the disease may search their memories more thoroughly than members of the unaffected ...
Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their ...
This statistics -related article is a stub. You can help Wikipedia by expanding it.
In statistical classification, the Bayes classifier is the classifier having the smallest probability of misclassification of all classifiers using the same set of features. [ 1 ] Definition
The fundamental prevalence-independent statistics are sensitivity and specificity.. Sensitivity or True Positive Rate (TPR), also known as recall, is the proportion of people that tested positive and are positive (True Positive, TP) of all the people that actually are positive (Condition Positive, CP = TP + FN).
Information bias (epidemiology), bias arising in a clinical study because of misclassification of the level of exposure to the agent or factor being assessed and/or misclassification of the disease or other outcome itself. Information bias (psychology), a type of cognitive bias, involving e.g. distorted evaluation of information.
Misclassified input data gain a higher weight and examples that are classified correctly lose weight. [note 1] Thus, future weak learners focus more on the examples that previous weak learners misclassified. An illustration presenting the intuition behind the boosting algorithm, consisting of the parallel learners and weighted dataset