Decision tree learning - Wikipedia
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 classification trees; in these tree structures, leaves represen
Rolling with Ruby on Rails
Now, next, and beyond: Tracking need-to-know trends at the intersection of business and technology AI/ML Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML). Future of the Firm Everything from new organizational structures and payment schemes to new expectations, skills, and tools will shape the future of the fi
Naive Bayes classifier - Wikipedia
Example of a naive Bayes classifier depicted as a Bayesian Network In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models.[1] Naive
Bayesian inference - Wikipedia
Toggle Inference over exclusive and exhaustive possibilities subsection
Bayesian probability - Wikipedia
Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən)[1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation[2] representing a state of knowledge[3] or as quantification of a personal belief.[4] The Bayesian interpretation of probability can be seen as an extension of
Bayesian average - Wikipedia
A Bayesian average is a method of estimating the mean of a population using outside information, especially a pre-existing belief,[1] which is factored into the calculation. This is a central feature of Bayesian interpretation. This is useful when the available data set is small.[2] Calculating the Bayesian average uses the prior mean m and a constant C. C is chosen based on the typical data set s
Currying - Wikipedia
This article is about the mathematical technique. For the cooking process of this name, see Curry. For the leather finishing process, see Currier. For horse grooming, see Currycomb. In mathematics and computer science, currying is the technique of translating a function that takes multiple arguments into a sequence of families of functions, each taking a single argument. In the prototypical exampl
linkiblog | How to Build a Popularity Algorithm You can be Proud of
<< Asp.net Localization: How to Access a Local Resource from Outside the Page | Many web sites allow users to casts vote on items. These visitors' votes are then often used to detect the items' "popularity" and hence rank the rated items accordingly. And when "rank" comes into play things gets tricky: The system can have inherent deficiencies in ranking items. That is mostly because develope
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Boyer–Moore - Wikipedia
Boost - Wikipedia