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Binary or binomial classification is the task of classifying the elements of a given set into two groups predicting which group each one belongs to on the basis of a classification rule. Contexts requiring a decision as to whether or not an item has some qualitative property , some specified characteristic, or some typical binary classification include:. Binary classification is dichotomization applied to practical purposes, and in many practical binary classification problems, the two groups are not symmetric — rather than overall accuracy, the relative proportion of different types of errors is of interest.

For example, in medical testing, a false positive detecting a disease when it is not present is considered differently from a false negative not detecting a disease when it is present.

Statistical classification is a problem studied in machine learning. It is a type of supervised learning , a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories. When there are only two categories the problem is known as statistical binary classification. Each classifier is best in only a select domain based upon the number of observations, the dimensionality of the feature vector , the noise in the data and many other factors.

For example random forests perform better than SVM classifiers for 3D point clouds. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals. For example, in medicine sensitivity and specificity are often used, while in information retrieval precision and recall are preferred. An important distinction is between metrics that are independent on the prevalence how often each category occurs in the population , and metrics that depend on the prevalence — both types are useful, but they have very different properties.

Given a classification of a specific data set, there are four basic data: There are eight basic ratios that one can compute from this table, which come in four complementary pairs each pair summing to 1. These are obtained by dividing each of the four numbers by the sum of its row or column, yielding eight numbers, which can be referred to generically in the form "true positive row ratio" or "false negative column ratio", though there are conventional terms.

There are thus two pairs of column ratios and two pairs of row ratios, and one can summarize these with four numbers by choosing one ratio from each pair — the other four numbers are the complements. These are the proportion of the population with the condition resp. These are the proportion of the population with a given test result for which the test is correct or, complementarily, for which the test is incorrect ; these depend on prevalence.

In diagnostic testing, the main ratios used are the true column ratios — True Positive Rate and True Negative Rate — where they are known as sensitivity and specificity. In informational retrieval, the main ratios are the true positive ratios row and column — Positive Predictive Value and True Positive Rate — where they are known as precision and recall.

One can take ratios of a complementary pair of ratios, yielding four likelihood ratios two column ratio of ratios, two row ratio of ratios. This is primarily done for the column condition ratios, yielding likelihood ratios in diagnostic testing.

Taking the ratio of one of these groups of ratios yields a final ratio, the diagnostic odds ratio DOR. There are a number of other metrics, most simply the accuracy or Fraction Correct FC , which measures the fraction of all instances that are correctly categorized; the complement is the Fraction Incorrect FiC.

The F-score combines precision and recall into one number via a choice of weighing, most simply equal weighing, as the balanced F-score F1 score. Some metrics come from regression coefficients: Other metrics include Youden's J statistic , the uncertainty coefficient , the Phi coefficient, and Cohen's kappa.

Tests whose results are of continuous values, such as most blood values , can artificially be made binary by defining a cutoff value , with test results being designated as positive or negative depending on whether the resultant value is higher or lower than the cutoff. However, such conversion causes a loss of information, as the resultant binary classification does not tell how much above or below the cutoff a value is. As a result, when converting a continuous value that is close to the cutoff to a binary one, the resultant positive or negative predictive value is generally higher than the predictive value given directly from the continuous value.

In such cases, the designation of the test of being either positive or negative gives the appearance of an inappropriately high certainty, while the value is in fact in an interval of uncertainty. On the other hand, a test result very far from the cutoff generally has a resultant positive or negative predictive value that is lower than the predictive value given from the continuous value. From Wikipedia, the free encyclopedia. This article needs additional citations for verification.

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