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Information Gain Ratio
In decision tree learning, information gain ratio is a ratio of information gain to the intrinsic information. It was proposed by Ross Quinlan, to reduce a bias towards multi-valued attributes by taking the number and size of branches into account when choosing an attribute. Information gain is also known as mutual information. Information gain calculation Information gain is the reduction in entropy produced from partitioning a set with attributes a and finding the optimal candidate that produces the highest value: : \text(T,a) = \Eta - \Eta, where T is a random variable and \Eta is the entropy of T given the value of attribute a . The information gain is equal to the total entropy for an attribute if for each of the attribute values a unique classification can be made for the result attribute. In this case the relative entropies subtracted from the total entropy are 0. Split information calculation The split information value for a test is defined as follow ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Tree Learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called Statistical classification, classification decision tree, trees; in these tree structures, leaf node, leaves represent class labels and branches represent Logical conjunction, conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression analysis, regression decision tree, trees. More generally, the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities such as categorical sequences. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Gain In Decision Trees
In the context of decision trees in information theory and machine learning, information gain refers to the conditional expected value of the Kullback–Leibler divergence of the univariate probability distribution of one variable from the conditional distribution of this variable given the other one. (In broader contexts, ''information gain'' can also be used as a synonym for either Kullback–Leibler divergence or mutual information, but the focus of this article is on the more narrow meaning below.) Explicitly, the ''information gain'' of a random variable X obtained from an observation of a random variable A taking value a is defined as: \mathit(X, a) = D_\text\bigl(P_ \parallel P_X\bigr) In other words, it is the Kullback–Leibler divergence of P_X(x) (the prior distribution for X) from P_(x) (the posterior distribution for X given A = a). The expected value of the information gain is the mutual information I(X; A): \operatorname_A mathit(X, A)= I(X; A) i.e. the reduct ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ross Quinlan
John Ross Quinlan is a computer science researcher in data mining and decision theory. He has contributed extensively to the development of decision tree algorithms, including inventing the canonical C4.5 and ID3 algorithms. He also contributed to early ILP literature with First Order Inductive Learner (FOIL). He is currently running the companRuleQuest Researchwhich he founded in 1997. Education He received his BSc degree in Physics and Computing from the University of Sydney in 1965 and his computer science doctorate at the University of Washington in 1968. He has held positions at the University of New South Wales, University of Sydney, University of Technology Sydney, and RAND Corporation. Artificial intelligence Quinlan is a specialist in artificial intelligence, particularly in the aspect involving machine learning and its application to data mining. He is a Founding Fellow of the Association for the Advancement of Artificial Intelligence. ID3 Ross Quinlan invented th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mutual Information
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual Statistical dependence, dependence between the two variables. More specifically, it quantifies the "Information content, amount of information" (in Units of information, units such as shannon (unit), shannons (bits), Nat (unit), nats or Hartley (unit), hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of Entropy (information theory), entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable. Not limited to real-valued random variables and linear dependence like the Pearson correlation coefficient, correlation coefficient, MI is more general and determines how different the joint distribution of the pair (X,Y) is from the product of the marginal distributions of X and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Entropy (information Theory)
In information theory, the entropy of a random variable quantifies the average level of uncertainty or information associated with the variable's potential states or possible outcomes. This measures the expected amount of information needed to describe the state of the variable, considering the distribution of probabilities across all potential states. Given a discrete random variable X, which may be any member x within the set \mathcal and is distributed according to p\colon \mathcal\to[0, 1], the entropy is \Eta(X) := -\sum_ p(x) \log p(x), where \Sigma denotes the sum over the variable's possible values. The choice of base for \log, the logarithm, varies for different applications. Base 2 gives the unit of bits (or "shannon (unit), shannons"), while base Euler's number, ''e'' gives "natural units" nat (unit), nat, and base 10 gives units of "dits", "bans", or "Hartley (unit), hartleys". An equivalent definition of entropy is the expected value of the self-information of a v ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outlook Sunny Branch Decision Tree
Outlook or The Outlook may refer to: Computing * Microsoft Outlook, also referred to as ''the classic Outlook'' an e-mail client and personal information management software product from Microsoft * Outlook for Windows, also referred to as ''the new Outlook'' * Outlook.com, a web mail service from Microsoft * Outlook on the web, a suite of web applications by Microsoft for Outlook.com, Office 365, Exchange Server, and Exchange Online * Outlook Express, an e-mail and news client bundled with earlier versions of Microsoft Windows Places * Outlook, Montana, a town in Montana, United States * Outlook, Saskatchewan, a town in Saskatchewan, Canada * Outlook, Washington, a town in Yakima Valley of Washington State * Outlook Peak, a mountain on Axel Heiberg Island, Nunavut, Canada Printed media Media companies * ''Outlook Media'', a company that publishes ''Outlook Columbus'', a GLBT magazine based in Columbus, Ohio Magazines * ''Outlook'' (Indian magazine), a weekly English-language ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Tree Learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called Statistical classification, classification decision tree, trees; in these tree structures, leaf node, leaves represent class labels and branches represent Logical conjunction, conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression analysis, regression decision tree, trees. More generally, the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities such as categorical sequences. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Telephone Number
A telephone number is the address of a Telecommunications, telecommunication endpoint, such as a telephone, in a telephone network, such as the public switched telephone network (PSTN). A telephone number typically consists of a Number, sequence of digits, but historically letters were also used in connection with telephone exchange names. Telephone numbers facilitate the switching and routing of telephone call, calls using a system of destination code routing. Telephone numbers are entered or dialed by a calling party on the originating telephone set, which transmits the sequence of digits in the process of signaling to a telephone exchange. The exchange completes the call either to another locally connected subscriber or via the PSTN to the called party. Telephone numbers are assigned within the framework of a national or regional telephone numbering plan to subscribers by telephone service operators, which may be commercial entities, state-controlled administrations, or ot ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Credit Card Number
A payment card number, primary account number (PAN), or simply a card number, is the card identifier found on payment cards, such as credit cards and debit cards, as well as stored-value cards, gift cards and other similar cards. In some situations the card number is referred to as a bank card number. The card number is primarily a card identifier and may not directly identify the bank account number(s) to which the card is/are linked by the issuing entity. The card number prefix identifies the issuer of the card, and the digits that follow are used by the issuing entity to identify the cardholder as a customer and which is then associated by the issuing entity with the customer's designated bank accounts. In the case of stored-value type cards, the association with a particular customer is only made if the prepaid card is reloadable. Card numbers are allocated in accordance with ISO/IEC 7812. The card number is typically embossed on the front of a payment card, and is encoded ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Training Set
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the correspondi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Trees
A decision tree is a decision support system, decision support recursive partitioning structure that uses a Tree (graph theory), tree-like Causal model, model of decisions and their possible consequences, including probability, chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in Decision tree learning, machine learning. Overview A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. In decision analysis, a de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification Algorithms
Classification is the activity of assigning objects to some pre-existing classes or categories. This is distinct from the task of establishing the classes themselves (for example through cluster analysis). Examples include diagnostic tests, identifying spam emails and deciding whether to give someone a driving license. As well as 'category', synonyms or near-synonyms for 'class' include 'type', 'species', 'order', 'concept', 'taxon', 'group', 'identification' and 'division'. The meaning of the word 'classification' (and its synonyms) may take on one of several related meanings. It may encompass both classification and the creation of classes, as for example in 'the task of categorizing pages in Wikipedia'; this overall activity is listed under taxonomy. It may refer exclusively to the underlying scheme of classes (which otherwise may be called a taxonomy). Or it may refer to the label given to an object by the classifier. Classification is a part of many different kinds of activ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
