Search results
Results from the WOW.Com Content Network
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
If not known and calculated from data, an accuracy comparison test could be made using "Two-proportion z-test, pooled for Ho: p1 = p2". Not used very much is the complementary statistic, the fraction incorrect (FiC): FC + FiC = 1, or (FP + FN)/(TP + TN + FP + FN) – this is the sum of the antidiagonal , divided by the total population.
Both precision and recall may be useful in cases where there is imbalanced data. However, it may be valuable to prioritize one metric over the other in cases where the outcome of a false positive or false negative is costly. For example, in medical diagnosis, a false positive test can lead to unnecessary treatment and expenses.
Adaptation to make precision reflect standard deviation instead of total interval between maximum and minimum value. 14:29, 14 April 2013: 520 × 280 (9 KB) Sv1xv {{Information |Description ={{en|1=Accuracy (truness and precision) according to BIMP and ISO 5725-1.
Precision takes all retrieved documents into account. It can also be evaluated considering only the topmost results returned by the system using Precision@k. Note that the meaning and usage of "precision" in the field of information retrieval differs from the definition of accuracy and precision within other branches of science and statistics.
Precision and recall. In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly ...
Validation of analytical procedures is imperative in demonstrating that a drug substance is suitable for a particular purpose. [5] Common validation characteristics include: accuracy, precision (repeatability and intermediate precision), specificity, detection limit, quantitation limit, linearity, range, and robustness.
Coding: Since test strips may vary from batch to batch, some models require a code to be provided, either by the user or on a plug-in chip supplied with each batch of test strips, to calibrate the meter to the strips of the batch. An incorrect code can cause errors of up to 4 mmol/L (72 mg/dL), with possibly serious consequences, including risk ...