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Confusion matrix is not limited to binary classification and can be used in multi-class classifiers as well. The confusion matrices discussed above have only two conditions: positive and negative. For example, the table below summarizes communication of a whistled language between two speakers, with zero values omitted for clarity. [20]
A chart of accounts (COA) is a list of financial accounts and reference numbers, grouped into categories, such as assets, liabilities, equity, revenue and expenses, and used for recording transactions in the organization's general ledger. Accounts may be associated with an identifier (account number) and a caption or header and are coded by ...
The measure precision at k, for example, is a measure of precision looking only at the top ten (k=10) search results. More sophisticated metrics, such as discounted cumulative gain, take into account each individual ranking, and are more commonly used where this is important.
The example above is the simplest kind of contingency table, a table in which each variable has only two levels; this is called a 2 × 2 contingency table. In principle, any number of rows and columns may be used. There may also be more than two variables, but higher order contingency tables are difficult to represent visually.
This is a list of abbreviations used in a business or financial context. ... COA – Chart of accounts; ... For example, $225K would be understood to mean $225,000 ...
The general ledger contains a page for all accounts in the chart of accounts [5] arranged by account categories. The general ledger is usually divided into at least seven main categories: assets, liabilities, owner's equity, revenue, expenses, gains and losses. [6]
The chart is the general guideline and every user can make any amendments and personally created accounts. The governments authorities accounting led by the Swedish National Financial Management Authority [2] and the communes led by Swedish Association of Local Authorities and Regions [3] [4] have special versions with adding special accounts for their purpose.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).