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Pay-per-view. Pay-per-view (PPV) is a type of pay television or webcast service that enables a viewer to pay to watch individual events via private telecast. Events can be purchased through a multichannel television platform using their electronic program guide, an automated telephone system, or through a live customer service representative.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances.
Pay-per-view (PPV) services are similar to subscription-based pay television services in that customers must pay to have the broadcast decrypted for viewing, but usually only entail a one-time payment for a single or time-limited viewing. Programs offered via pay-per-view are most often movies or sporting events, but may also include other ...
v. t. e. Pay-per-click (PPC) is an internet advertising model used to drive traffic to websites, in which an advertiser pays a publisher (typically a search engine, website owner, or a network of websites) when the ad is clicked. [1][2] Pay-per-click is usually associated with first-tier search engines (such as Google Ads, Amazon Advertising ...
COB – Close of Business. COC – Cost of Credit [2] or Cost of Capital [3] COD – Cost of Debt [4] or Cash on Delivery. COE – Center of Excellence or Cost of Equity [5] COGS – Cost of Goods Sold. Corp. – Corporation. COO – Chief Operating Officer. CPA – Certified Public Accountant. CPI – Consumer Price Index.
The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications.
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.