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===False Positive Rate (FPR) in Machine LearningIntroduction==The false positive rate (FPR), in A feature cross is a machine learning, is technique that adds interaction features between features in a measure for the percentage of negative instances that are incorrectly categorized as positivedataset. It is simply the sum of the number of falsely positive instances and the total number negative instances. FPR is often This technique can be used with the true positive rate, also known as recall or sensitivity, to evaluate the capture non-linear relationships among features and improve performance and effectiveness of a binary classifiermodel.

[[Sensitivity and Specificity|Sensitivity]] and [[Specificity (medical test)|specificity]] are ==How it works==A feature cross creates new features by combining two or more existing features. If we have two other measures features, for example, x1 + x2, a feature crossover would create a new feature, which is x1 *x2. This new feature captures the interaction of x1-x2 and can improve a binary classifiermodel's performance that are related to the FPR. Specificity is the ratio of true negatives to all negatives, while Sensitivity measures the ratio of true positives to all positives.

In a [[Receiver operating characteristic|receiver operating characteristic (ROC) curve]]==Examples==A feature cross can be seen in the context polynomial regression. Polynomial regression allows us to add polynomial terms such as x12,x1*x2,x22, and so on. to capture non-linear relationships among the FPR features. Another example is plotted against the true positive rate. The ROC curve shows the tradecontext of natural-off language processing (NLP), which allows us to use feature crosses to capture interaction between FPR and TPR at different threshold settings for words within a binary classifiersentence. In the sentence "The ROC curve cat sat on a mat", we can be used create new features to indicate capture the performance of interaction between "cat", "sat", or "on" and the classifier in general. Each point represents a threshold setting"mat."

False positive rate (FPR) can be used to determine how often A feature cross is a test which is supposed way to give combine words. If you have the words "nocat" actually gives or "yessat", you could make a new word by adding them together. False positives This can be for example when help a test claims you have a disease but you don't really have it. It is important to computer better understand how often this happens in order to decide the relationship between words, especially if the test should be usedthey are related.