191 Sentences With "decision tree" | Random Sentence Generator

A company employee will verify a customer's identity and follow a decision-tree.

You have to remember that an algorithm is just an automated decision tree.

Under the hood, Facebook's rollout technique takes the form of a decision tree.

Once built, the decision tree can forecast how the new offender might behave.

At the time, it had been merely one decision tree among many others.

We're here to help, with a scientific guide to navigating that daily food decision tree.

After exhausting this "decision tree" of possible variables, the computer will spit out a decision.

How it works: The patent describes a system that uses a decision tree to classify users.

But in order to buy a smartwatch, you have to work your way down a decision tree.

For the med reminders, we're piloting the text solution now to ensure the clinical decision tree is comprehensive.

Games like Tic Tac Toe are simple enough that they can be completely solved in a decision tree.

Instead, there are a lot of compromises and an entire decision tree to run through based on your priorities.

Attracted by what looked at first like an easy bargain, neither player looked down the decision tree to the end.

When somebody wants to go buy a watch, offering them a decision tree instead isn't ideal (to say the least).

The algorithm essentially works like a decision tree in which each branch splits the data set according to some statistical feature.

It also happens to be very structured and formulaic, which lends itself well to a chabot whose primary "intelligence" is a decision tree.

If you're going down the decision tree of yes-or-no and it's unclear how the repayment process will pan out, walk away.

For those brands and companies who elect to use it with customers, I imagine an added layer of interactivity and decision-tree-like qualifiers.

It is entirely fine that Google wanted to use live data to train their decision tree processes; but that process is not direct care.

Each installment is narrated in the second person and allows readers to guide the course of the decision-tree tales at critical plot points.

The management protocols provide a step-by-step decision tree to ensure the proper treatment of those who are subjected to a major explosion.

The reason: there's no decision-tree in part because the issue has grown so unmanageable that no one wants to own what goes awry.

Rather, patients' responses are used to determine which additional questions they get asked to pull out other relevant information — in a classic decision tree algorithm.

There are plenty of procedurally generated images out there (like Google Deep Dream) that rely on "Neural networks," usually an advanced Python-like decision tree.

Every choice the protagonist makes in "Fierce Kingdom," the expertly made new thriller by Gin Phillips, is another precarious step up a gnarled decision tree.

AlphaGo, for its part, uses a Monte Carlo Tree Search heuristic algorithm, which quickly "searches" branches of a decision tree to select the AI's next move.

For example, the campus could create a decision tree that helps students identify when and where to reach out to get help with their specific concerns.

Using the data, you could imagine constructing a simple decision tree by hand, like the one below, using the characteristics of each offender to build a flowchart.

In later stages, those probes try to replicate the observed behavior of the network on a decision tree, rule-based structure, or another model that is interpretable.

How it works: Woebot uses the tools of cognitive behavioral therapy and relies on a decision tree that mirrors the decision-making of therapists while speaking with patients.

It was as though the researchers were groping to fill in some flowchart or decision tree, when, in reality, little of the action had unfolded methodically at all.

I seem to be forever surrendering my privacy in exchange for some short-term gain, rather than dutifully slogging through the decision tree of the cookie opt-out.

The development tool, available now through GitHub, breaks game creation down into a graphical interface, providing a sort of decision tree to map out the turn-by-turn game play.

The resulting decision tree is part of what makes buyers wind up spending so much more on pickup trucks, according to Jessica Caldwell, the executive director of insights for Edmunds.

Once built, a decision tree can be used as a flowchart: taking a set of circumstances and assessing step by step what to do, or, in this case, what will happen.

When we asked one of our visual editors, Allison McCann, to lay out these possibilities in graphic form, she returned several days later with sketches of a decision tree gone wild.

Zuckerberg is hoping to erect a scalable system, an orderly decision tree that accounts for every eventuality and exception, but the boundaries of speech are a bedevilling problem that defies mechanistic fixes.

They settled on a decision-tree machine learning approach that takes into account such factors as molecular weight and the various molecules in the body a particular drug is meant to target, among others.

Moderators then use a complex decision tree to determine if a particular piece of content or an account should be removed, and they receive updates on how to interpret the guidelines every two weeks.

A report in February by the Verge, a news site, found that a Facebook subcontractor's training regime required moderators to learn a decision-tree of rules, then justify which one led to a take-down.

In the upcoming match between Sedol and AlphaGo, DeepMind's artificial intelligence will have to sort through a much larger decision tree than IBM's Deep Blue in addition to sorting through a higher number of moves.

Instead of just creating a basic decision tree (this set of answers leads to this quiz result), the company's actually building out models that show how each question and answer is related to overall satisfaction.

The Trust, as data controller, will define any decision tree based algorithms to be applied to defined datasets or patient cohorts in line with NHS clinical guidance and will instructed [sic] DeepMind on all future developments.

And although most of Sophia's dialogue comes from a simple decision tree (the same tech used by chatbots; when you say X, it replies Y), what it says is integrated with these other inputs in a unique fashion.

"If you look at how we practice medicine today, it's more of an expert system, where you have a decision tree that you try to make fit into people's heads, and you call those people doctors," he said.

But where a chess program sorts through dozens of moves for each possible board position, a Go program must consider hundreds of possible moves, a computing task that grows exponentially with each further step in the decision tree.

The triage element is described as using decision tree protocols and an evidence based approach to help patients understand what might be wrong with them and to take the appropriate action, which isn't always to see their doctor.

Frostpunk is also a game that hasn't fully thought through a political decision tree that pulls you towards authoritarianism or theocracy, and probably doesn't impose enough trade-offs to any decisions to make their cost feel particularly resonant.

One of those weeks when you don't bother to look at your screen all that often; when you spend your time putting together the decision tree based upon varying scenarios, each dependent upon how the new administration moves forward.

With health care, if you look at how we practice medicine today, it's more of what we call an expert system, where you have a decision tree that you try to make fit into people's heads, and we call those doctors.

The patent, according to CBInsights, shows a decision tree that collects data points on a user's education level, travel history, the number of devices they own, homeownership and where they live to guess the probability of them falling in a given socioeconomic class.

Though I hadn't expected a full-on holo-Freud, the AI's vacillation between soulless call center decision tree script and /r/fellowkids-worthy fumbles with youthful parlance (don't use the smirk emoji unless you're trying to fuck me, Woebot) was leaving me as cold.

To that end Gamalon also released a new tool called Idea Studio, a product that can automatically build learning trees to help users arrive at answers extremely fast or allow a business analyst or data scientists to simply enter a series of queries and build a decision tree on the fly based on the text.

Thus, each thousand-page codebook from the various federal, state, city, township and independent agencies – all dictating interconnecting, location and structure-dependent needs – lead to an incredibly expansive decision tree that requires an endless set of simulations to fully understand all the options you have to reach compliance, and their respective cost-effectiveness and efficiency.

Some good news is that the Centers for Disease Control and Prevention has just released a series of updated guidelines for schools, including a useful "decision tree" for districts in making the call as to whether to close, how to go about it, and what to consider in the process, including the welfare of economically vulnerable children.

Parker explained that the vision is to use Kira to extract the information about the claim and claimant; auto-populate a claims management database; then use document automation to draft the submission to the court (automatically pulling information from the database about the claim that has been extracted by Kira, then automatically populating a template pleading); and then use expert logic to determine whether that case can be settled (applying a decision tree type structure — simple legal reasoning with a number of different variables).

Once the inputs are received a decision tree analysis tool uses the inputs; builds a step-by-step decision tree and makes a decision recommendation.

In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.

Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches. In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross QuinlanQuinlan, J. R. 1986.

The ID3 algorithm is used by training on a data set S to produce a decision tree which is stored in memory. At runtime, this decision tree is used to classify new test cases (feature vectors) by traversing the decision tree using the features of the datum to arrive at a leaf node. The class of this terminal node is the class the test case is classified as.

In mathematics, an evasive Boolean function ƒ (of n variables) is a Boolean function for which every decision tree algorithm has running time of exactly n. Consequently, every decision tree algorithm that represents the function has, at worst case, a running time of n.

Each query may be dependent on previous queries. Several variants of decision tree models have been introduced, depending on the complexity of the operations allowed in the computation of a single comparison and the way of branching. Decision trees models are instrumental in establishing lower bounds for complexity theory for certain classes of computational problems and algorithms. The computational complexity of a problem or an algorithm expressed in terms of the decision tree model is called its decision tree complexity or query complexity.

Here is a short list of incremental decision tree methods, organized by their (usually non-incremental) parent algorithms.

The Extremely Fast Decision Tree learnerManapragada, C., Webb, G. I., Salehi, M. (2018) Extremely Fast Decision Tree. Proceedings KDD 2018, ACM Press, New York, NY, USA, pp. 1953-1962. is statistically more powerful than VFDT, allowing it to learn more detailed trees from less data. It differs from VFDT in the method for deciding when to insert a new branch into the tree.

Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making). This page deals with decision trees in data mining.

Deterministic decision trees also require exponential size to detect cliques, or large polynomial size to detect cliques of bounded size.. The Aanderaa–Karp–Rosenberg conjecture also states that the randomized decision tree complexity of non-trivial monotone functions is . The conjecture again remains unproven, but has been resolved for the property of containing a clique for . This property is known to have randomized decision tree complexity .For instance, this follows from .

Once the decision tree is constructed, then the new branches that can be added productively to the tree are identified. Then they are grafted to the existing tree to improve the decision making process. Pruning and Grafting are complementary methods to improve the decision tree in supporting the decision. Pruning allows cutting parts of decision trees to give more clarity and Grafting adds nodes to the decision trees to increase the predictive accuracy.

Lower bounds in communication complexity can be used to prove lower bounds in decision tree complexity, VLSI circuits, data structures, streaming algorithms, space–time tradeoffs for Turing machines and more.

There are several diversions, such as the Testing Grounds where crew for the Galleykeep are recruited, but although they have a long decision tree all paths lead to death or failure.

See , Chapter 12, "Decision trees", pp. 259–269. Because the property of containing a clique is monotone, it is covered by the Aanderaa–Karp–Rosenberg conjecture, which states that the deterministic decision tree complexity of determining any non-trivial monotone graph property is exactly . For arbitrary monotone graph properties, this conjecture remains unproven. However, for deterministic decision trees, and for any in the range , the property of containing a -clique was shown to have decision tree complexity exactly by .

Ross Quinlan invented the Iterative Dichotomiser 3 (ID3) algorithm which is used to generate decision trees. ID3 follows the principle of Occam's razor in attempting to create the smallest decision tree possible.

Grafting is the process of adding nodes to inferred decision trees to improve the predictive accuracy. A decision tree is a graphical model that is used as a support tool for decision process.

FAST-ER detector is an improvement of the FAST detector using a metaheuristic algorithm, in this case simulated annealing. So that after the optimization, the structure of the decision tree would be optimized and suitable for points with high repeatability. However, since simulated annealing is a metaheurisic algorithm, each time the algorithm would generate a different optimized decision tree. So it is better to take efficiently large amount of iterations to find a solution that is close to the real optimal.

A simple decision tree to detect the presence of a 3-clique in a 4-vertex graph. It uses up to 6 questions of the form "Does the red edge exist?", matching the optimal bound n(n − 1)/2. The (deterministic) decision tree complexity of determining a graph property is the number of questions of the form "Is there an edge between vertex and vertex ?" that have to be answered in the worst case to determine whether a graph has a particular property.

That is, it is the minimum height of a boolean decision tree for the problem. There are possible questions to be asked. Therefore, any graph property can be determined with at most questions. It is also possible to define random and quantum decision tree complexity of a property, the expected number of questions (for a worst case input) that a randomized or quantum algorithm needs to have answered in order to correctly determine whether the given graph has the property.

C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan.Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993. C4.5 is an extension of Quinlan's earlier ID3 algorithm.

Decision Tree Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data. Data mining in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures. In the easiest case, a rule is expressed with “if- then statements” and was created with the ID3 algorithm for decision tree learning.

RevoScaleR is a machine learning package in R created by Microsoft. It is available as part of Machine Learning Server, Microsoft R Client, and Machine Learning Services in Microsoft SQL Server 2016. The package contains functions for creating linear model, logistic regression, random forest, decision tree and boosted decision tree, and K-means, in addition to some summary functions for inspecting and visualizing data.. It has a Python package counterpart called revoscalepy. Another closely related package is MicrosoftML, which contains machine learning algorithms that RevoScaleR does not have, such as neural network and SVM.

Inductive learning had been divided into two types: decision tree (DT) and covering algorithms (CA). DTs discover rules using decision tree based on the concept of divide-and-conquer, while CA directly induces rules from the training set based on the concept of separate and conquers. Although DT algorithms was well recognized in the past few decades, CA started to attract the attention due to its direct rule induction property, as emphasized by Kurgan et al. [1]. Under this type of inductive learning approach, several families have been developed and improved.

Information gain ratio biases the decision tree against considering attributes with a large number of distinct values. So it solves the drawback of information gain—namely, information gain applied to attributes that can take on a large number of distinct values might learn the training set too well. For example, suppose that we are building a decision tree for some data describing a business's customers. Information gain is often used to decide which of the attributes are the most relevant, so they can be tested near the root of the tree.

Many data mining software packages provide implementations of one or more decision tree algorithms. Examples include Salford Systems CART (which licensed the proprietary code of the original CART authors), IBM SPSS Modeler, RapidMiner, SAS Enterprise Miner, Matlab, R (an open-source software environment for statistical computing, which includes several CART implementations such as rpart, party and randomForest packages), Weka (a free and open-source data- mining suite, contains many decision tree algorithms), Orange, KNIME, Microsoft SQL Server , and scikit-learn (a free and open-source machine learning library for the Python programming language).

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 decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.

A branching identification key is a presentation form of a single-access key where the structure of the decision tree is displayed graphically as a branching structure, involving lines between items.Winston, J. 1999. Describing Species. Columbia University Press.

OpenL Tablets is a business rule management system (BRMS) and a business rules engine (BRE) based on table representation of rules. Engine implements optimized sequential algorithm. OpenL includes such table types as decision table, decision tree, spreadsheet-like calculator.

Complexity measures are very generally defined by the Blum complexity axioms. Other complexity measures used in complexity theory include communication complexity, circuit complexity, and decision tree complexity. The complexity of an algorithm is often expressed using big O notation.

It was also the first version to support JSL, JMP Scripting Language. In 2005, data mining tools like a decision tree and neural net were added with version 5 as well as Linux support, which was later withdrawn in JMP 9.

The data portability right is related to the "right to explanation", i.e. when automated decisions are made that have legal effect or significant impact on individual data subjects. How to display an algorithm? One way is through a decision tree.

Forests are like the pulling together of decision tree algorithm efforts. Taking the teamwork of many trees thus improving the performance of a single random tree. Though not quite similar, forests give the effects of a K-fold cross validation.

In computational complexity theory, a certificate (also called a witness) is a string that certifies the answer to a computation, or certifies the membership of some string in a language. A certificate is often thought of as a solution path within a verification process, which is used to check whether a problem gives the answer "Yes" or "No". In the decision tree model of computation, certificate complexity is the minimum number of the n input variables of a decision tree that need to be assigned a value in order to definitely establish the value of the Boolean function f.

Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent 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 trees.

Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent 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 trees.

Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106 used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains.

Decision tree learning algorithms can be applied to learn to predict a dependent variable from data. Although the original Classification And Regression Tree (CART) formulation applied only to predicting univariate data, the framework can be used to predict multivariate data, including time series.

However efficient computation and joint estimation of all model parameters (including the breakpoints) may be obtained by an iterative procedure currently implemented in the package `segmented` for the R language. A variant of decision tree learning called model trees learns piecewise linear functions.

Much of the information in a decision tree can be represented more compactly as an influence diagram, focusing attention on the issues and relationships between events. The rectangle on the left represents a decision, the ovals represent actions, and the diamond represents results.

The nondeterministic decision tree complexity of a function is known more commonly as the certificate complexity of that function. It measures the number of input bits that a nondeterministic algorithm would need to look at in order to evaluate the function with certainty.

Retrieved May 20, 2018. The program was called the "Prudent Diet" and had been developed in the 1950s by Dr. Norman Jolliffe, head of the board's Bureau of Nutrition.Goetz, Thomas. The Decision Tree: How to Make Better Choices and Take Control of Your Health.

Evolutionary algorithms have been used to avoid local optimal decisions and search the decision tree space with little a priori bias. It is also possible for a tree to be sampled using MCMC. The tree can be searched for in a bottom-up fashion.

Working alongside his father, he pioneered the development of decision-tree tax-preparation software. However, H&R; Block was not interested in this new way of doing business. This led Hewitt to found a tax-preparation service that would exploit the efficiency of the new technology.

The lines out of the vertex represent a possible action for that player. The payoffs are specified at the bottom of the tree. The extensive form can be viewed as a multi-player generalization of a decision tree. To solve any extensive form game, backward induction must be used.

ID3-generated decision tree used to determine whether a particular nucleotide pair within a pre-mRNA sequence corresponds to an mRNA splice site. This tree has been shown to have a 95% correct prediction rate. ID3 does not guarantee an optimal solution. It can converge upon local optima.

In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model). In the logistic variant, the LogitBoost algorithm is used to produce an LR model at every node in the tree; the node is then split using the C4.5 criterion. Each LogitBoost invocation is warm-started from its results in the parent node.

Litigation risk analysis is a subset of decision tree analysis and is the application of decision tree analysis to litigation and lawsuits. It operates based on the idea that prosecutors do not prosecute all cases even if they have merit due to several factors such as economic considerations. This method can reveal the odds of winning a case or financial loss that a litigation would incur, especially if it is too great for the kind of offence involved or there is an imbalance between effort and need. With this tool, a prosecutor is in a better position to decide whether to pursue a case or offer a plea bargain if this is permitted.

A fast-and-frugal tree is a heuristic that allows to make a classifications, such as whether a patient with severe chest pain is likely to have a heart attack or not, or whether a car approaching a checkpoint is likely to be a terrorist or a civilian. It is called “fast and frugal” because, just like take-the-best, it allows for quick decisions with only few cues or attributes. It is called a “tree” because it can be represented like a decision tree in which one asks a sequence of questions. Unlike a full decision tree, however, it is an incomplete tree – to save time and reduce the danger of overfitting.

INTERNIST-I worked best when only a single disease was expressed in the patient, but handled complex cases poorly, where more than one disease was present. This was because the system exclusively relied on hierarchical or taxonomic decision-tree logic, which linked each disease profile to only one “parent” disease class.

Recursively partitioning is method that creates a decision tree using qualitative data. Understanding the way rules break classes up with a low error of misclassification while repeating each step until no sensible splits can be found. However, recursive partitioning can have poor prediction ability potentially creating fine models at the same rate.

Official Forum Clarification on Hunch's History: In December 2009, Wikipedia co-founder Jimmy Wales joined Hunch.com as Board Member and advisor. By February 2010, the website's traffic had grown to 1.2 million unique visitors. In January 2011 Hunch was redesigned from decision tree system and topic specific questions to tagging and product referral.

A decision tree consists of three types of nodes: # Decision nodes – typically represented by squares # Chance nodes – typically represented by circles # End nodes – typically represented by triangles Decision trees are commonly used in operations research and operations management. If, in practice, decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm. Another use of decision trees is as a descriptive means for calculating conditional probabilities. Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods.

The credibility in Twitter events by creating a data set of tweets that is relevant to trending topics was detected. Using crowd sourcing, they annotated the data sets regarding the veracity of each tweet. 4 features, namely message, user, topic and propagation were analysed using a Decision Tree Model. This method achieved 86% accuracy.

Decision tree learning is a powerful classification technique. The tree tries to infer a split of the training data based on the values of the available features to produce a good generalization. The algorithm can naturally handle binary or multiclass classification problems. The leaf nodes can refer to any of the K classes concerned.

The basic idea from which the data structure was created is the Shannon expansion. A switching function is split into two sub-functions (cofactors) by assigning one variable (cf. if-then-else normal form). If such a sub-function is considered as a sub-tree, it can be represented by a binary decision tree.

Fitting the training set too closely can lead to degradation of the model's generalization ability. Several so-called regularization techniques reduce this overfitting effect by constraining the fitting procedure. One natural regularization parameter is the number of gradient boosting iterations M (i.e. the number of trees in the model when the base learner is a decision tree).

Project valuation via decision tree. Corporate finance theory has also been extended: mirroring the above developments, asset-valuation and decisioning no longer need assume "certainty". Monte Carlo methods in finance allow financial analysts to construct "stochastic" or probabilistic corporate finance models, as opposed to the traditional static and deterministic models; see . Relatedly, Real Options theory allows for owner—i.e.

One of the input attributes might be the customer's credit card number. This attribute has a high information gain, because it uniquely identifies each customer, but we do not want to include it in the decision tree: deciding how to treat a customer based on their credit card number is unlikely to generalize to customers we haven't seen before.

Drawn from left to right, a decision tree has only burst nodes (splitting paths) but no sink nodes (converging paths). Therefore, used manually, they can grow very big and are then often hard to draw fully by hand. Traditionally, decision trees have been created manually – as the aside example shows – although increasingly, specialized software is employed.

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 company RuleQuest Research which he founded in 1997.

One of the input attributes might be the customer's credit card number. This attribute has a high mutual information, because it uniquely identifies each customer, but we do not want to include it in the decision tree: deciding how to treat a customer based on their credit card number is unlikely to generalize to customers we haven't seen before (overfitting). To counter this problem, Ross Quinlan proposed to instead choose the attribute with highest information gain ratio from among the attributes whose information gain is average or higher. This biases the decision tree against considering attributes with a large number of distinct values, while not giving an unfair advantage to attributes with very low information value, as the information value is higher or equal to the information gain.

Examples of other algorithms used for deterioration modeling are decision tree, k-NN, random forest, gradient boosting trees, random forest regression, and naive Bayes classifier. In this type model usually, the deterioration is predicted using a set of input variables or predictive features. The examples of predictive features used in the literature are initial condition, traffic, climatic features, pavement type and road class.

A zero-suppressed decision diagram (ZSDD or ZDD) is a particular kind of binary decision diagram (BDD) with fixed variable ordering. This data structure provides a canonically compact representation of sets, particularly suitable for certain combinatorial problems. Recall the OBDD reduction strategy i.e. a node is removed from the decision tree if both out-edges point to the same node.

It uses a greedy strategy by selecting the locally best attribute to split the dataset on each iteration. The algorithm's optimality can be improved by using backtracking during the search for the optimal decision tree at the cost of possibly taking longer. ID3 can overfit the training data. To avoid overfitting, smaller decision trees should be preferred over larger ones.

Thus the growth diagnostics methodology departs from the "symptoms" of low growth that are visible in a country's economy—for example, low investment. Using a decision tree, all possible causes of these symptoms are inspected and if possible, eliminated. Next, the causes of these causes are scrutinized. This goes on until the most binding constraint to growth in a country is found.

It is a trivial task when the convex polygon is specified in a traditional for polygons way, i.e., by the ordered sequence of its vertices v_1,\dots, v_m. When the input list of vertices (or edges) is unordered, the time complexity of the problems becomes O(m log m). A matching lower bound is known in the algebraic decision tree model of computation.

EPAM was written in IPL/V. The project was started in the late 1950s with the aim to learn nonsense syllables. The term nonsense is used because the learned patterns are not connected with a meaning but they are standing for their own. The software is working internally by creating a decision tree. An improved version is available under the name “EPAM-VI”.

AdaBoost (with decision trees as the weak learners) is often referred to as the best out-of-the-box classifier. When used with decision tree learning, information gathered at each stage of the AdaBoost algorithm about the relative 'hardness' of each training sample is fed into the tree growing algorithm such that later trees tend to focus on harder-to-classify examples.

Chess is one of the most well-known and frequently played strategy games. A strategy game or strategic game is a game (e.g. a board game) in which the players' uncoerced, and often autonomous, decision-making skills have a high significance in determining the outcome. Almost all strategy games require internal decision tree-style thinking, and typically very high situational awareness.

A medical algorithm for assessment and treatment of overweight and obesity. A medical algorithm is any computation, formula, statistical survey, nomogram, or look-up table, useful in healthcare. Medical algorithms include decision tree approaches to healthcare treatment (e.g., if symptoms A, B, and C are evident, then use treatment X) and also less clear-cut tools aimed at reducing or defining uncertainty.

Conceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980s. It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering methods are capable of generating hierarchical category structures; see Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis, decision tree learning, and mixture model learning.

Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.

The features from a decision tree or a tree ensemble are shown to be redundant. A recent method called regularized treeH. Deng, G. Runger, "Feature Selection via Regularized Trees", Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, 2012 can be used for feature subset selection. Regularized trees penalize using a variable similar to the variables selected at previous tree nodes for splitting the current node.

There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, decision tree, logistic regression, linear to multiple regression analysis, classification to do predictive modeling. It also includes Unsupervised Machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis.

IEEE TTS Workshop 2002. An index of the units in the speech database is then created based on the segmentation and acoustic parameters like the fundamental frequency (pitch), duration, position in the syllable, and neighboring phones. At run time, the desired target utterance is created by determining the best chain of candidate units from the database (unit selection). This process is typically achieved using a specially weighted decision tree.

A full decision tree, in contrast, requires 2n exits. The order of cues (tests) in a fast-and-frugal tree is determined by the sensitivity and specificity of the cues, or by other considerations such as the costs of the tests. In the case of the HIV tree, the ELISA is ranked first because it produces fewer misses than the Western blot test, and also is less expensive.

The decision tree can be linearized into decision rules, where the outcome is the contents of the leaf node, and the conditions along the path form a conjunction in the if clause. In general, the rules have the form: : if condition1 and condition2 and condition3 then outcome. Decision rules can be generated by constructing association rules with the target variable on the right. They can also denote temporal or causal relations.

The team should meet on a continuous basis for best communication. The team follows a decision-tree process to help determine if a threat made at the school is transient or serious. The multidisciplinary approach is preventive because it identities students that could be on the path to violence and intervenes to get them the wraparound services they could be needing to put them on a more positive path.

Therefore, in the general case the convex hull of n points cannot be computed more quickly than sorting. The standard Ω(n log n) lower bound for sorting is proven in the decision tree model of computing, in which only numerical comparisons but not arithmetic operations can be performed; however, in this model, convex hulls cannot be computed at all. Sorting also requires Ω(n log n) time in the algebraic decision tree model of computation, a model that is more suitable for convex hulls, and in this model convex hulls also require Ω(n log n) time.Preparata, Shamos, Computational Geometry, Chapter "Convex Hulls: Basic Algorithms" However, in models of computer arithmetic that allow numbers to be sorted more quickly than O(n log n) time, for instance by using integer sorting algorithms, planar convex hulls can also be computed more quickly: the Graham scan algorithm for convex hulls consists of a single sorting step followed by a linear amount of additional work.

Gray goo is a useful construct for considering low-probability, high-impact outcomes from emerging technologies. Thus, it is a useful tool in the ethics of technology. Daniel A. Vallero applied it as a worst-case scenario thought experiment for technologists contemplating possible risks from advancing a technology. This requires that a decision tree or event tree include even extremely low probability events if such events may have an extremely negative and irreversible consequence, i.e.

The Texas Medication Algorithm Project (TMAP) is a controversial decision-tree medical algorithm, the design of which was based on the expert opinions of mental health specialists. It has provided and rolled out a set of psychiatric management guidelines for doctors treating certain mental disorders within Texas' publicly funded mental health care system, along with manuals relating to each of them. The algorithms commence after diagnosis and cover pharmacological treatment (hence "Medication Algorithm").

Depending on the number of branches at a single point, a branching key may be dichotomous or polytomous. In a diagnostic key, the branching structure of the key should not be mistaken for a phylogenetic or cladistic branching pattern. All single-access keys form a decision tree (or graph if reticulation exists), and thus all such keys have a branching structure. "Branching key" may therefore occasionally be used as a synonym for single-access key.

A knob is also found on the lower right portion of the toy which controlled its volume and power. The "mouth" was reused detail molding taken from the Micronauts Battle Cruiser. At the bottom was a large slot for the 8-track cartridge tapes. This particular version was essentially a regular 8-track tape player, but by utilizing unique, clever, and patented mathematical decision tree programming methods, over 20 interactive modes of operation were achieved.

Apply the optimal algorithm recursively to this graph. The runtime of all steps in the algorithm is O(m), except for the step of using the decision trees. The runtime of this step is unknown, but it has been proved that it is optimal - no algorithm can do better than the optimal decision tree. Thus, this algorithm has the peculiar property that it is provably optimal although its runtime complexity is unknown.

The story begins with a strange voice (Peter Daltrey) calling out to the eight characters that are taken from various planes of time. The mysterious voice tells them they are in a place of "no-time and no-space". Urging them to continue, the voice gives them a task: to reach The Electric Castle and find out what's inside. After various steps, they come to the Decision Tree where the voice tells them one of them must die.

This algorithm usually produces small trees, but it does not always produce the smallest possible decision tree. ID3 is harder to use on continuous data than on factored data (factored data has a discrete number of possible values, thus reducing the possible branch points). If the values of any given attribute are continuous, then there are many more places to split the data on this attribute, and searching for the best value to split by can be time consuming.

Analytic functions in RevoScaleR takes in data source object, a compute context, and the other parameters needed to build the specific model, such as formula for the logistic regression or the number of trees in a decision tree. In addition to those parameters, one can also specify the level of parallelism, such as the size of the data chunk for each process or number of processes to build the model. However, parallelism is only available in non-express edition.

There are various structures of ripple-down rules, for example single-classification ripple-down rules (SCRDR), multiple-classification ripple-down rules (MCRDR), nested ripple-down rules (NRDR) and repeat-inference multiple-classification ripple-down rules (RIMCRDR). The data structure of RDR described here is SCRDR, which is the simplest structure. The data structure is similar to a decision tree. Each node has a rule, the format of this rule is IF cond1 AND cond2 AND ... AND condN THEN conclusion.

For example, in NMR the chemical shift axis may be discretized and coarsely binned, and in MS the spectral accuracies may be rounded to integer atomic mass unit values. Also, several digital camera systems incorporate an automatic pixel binning function to improve image contrast. Binning is also used in machine learning to speed up the decision- tree boosting method for supervised classification and regression in algorithms such as Microsoft's LightGBM and scikit-learn's Histogram-based Gradient Boosting Classification Tree.

Decision lists are a representation for Boolean functions which can be easily learnable from examples. Single term decision lists are more expressive than disjunctions and conjunctions; however, 1-term decision lists are less expressive than the general disjunctive normal form and the conjunctive normal form. The language specified by a k-length decision list includes as a subset the language specified by a k-depth decision tree. Learning decision lists can be used for attribute efficient learning.

Depending on the procedures used to estimate the value of the project under each scenario (and on the techniques used to estimate the probabilities of the scenarios), ECV can be a useful way to address project uncertainties. However, as indicated below, the technique often involves explanations that may or may not be appropriate. Typically, ECV represents a simplified version of ENPV often necessary for projects that produce new products. The project is broken down into stages which are represented in a decision tree.

Traditionally, decision trees have been created manually. A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including 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 machine learning.

Decision trees can also be seen as generative models of induction rules from empirical data. An optimal decision tree is then defined as a tree that accounts for most of the data, while minimizing the number of levels (or "questions").R. Quinlan, "Learning efficient classification procedures", Machine Learning: an artificial intelligence approach, Michalski, Carbonell & Mitchell (eds.), Morgan Kaufmann, 1983, p. 463–482. Several algorithms to generate such optimal trees have been devised, such as ID3/4/5,Utgoff, P. E. (1989).

Now the switching lemma guarantees that after setting some variables randomly, we end up with a Boolean function that depends only on few variables, i.e., it can be computed by a decision tree of some small depth d. This allows us to write the restricted function as a small formula in disjunctive normal form. A formula in conjunctive normal form hit by a random restriction of the variables can therefore be "switched" to a small formula in disjunctive normal form.

Using training data, RFD constructs a decision forest, consisting of many decision trees. Each decision tree evaluates several domains, and based on the presence or absence of interactions in these domains, makes a decision as to if the protein pair interacts. The vector representation of the protein pair is evaluated by each tree to determine if they are an interacting pair or a non-interacting pair. The forest tallies up all the input from the trees to come up with a final decision.

Eko software constructs audiovisual multimedia within which users have selection options for seamlessly streaming choices from a traversable video tree. This process is explained and exemplified by actual use examples including the interactive video made for Bob Dylan's Like a Rolling Stone. The availability of different video streams allows for a change in viewer perspective or for narrative- branching. For example, a decision tree based on 174 individual film segments results in users controlling an extremely large (98,304) number of permutations.

Each processor may perform a noisy broadcast to all other processors where the received bits may be independently flipped with a fixed probability. The problem is for processor P_0 to determine f(x_1,x_2,\ldots,x_n) for some function f. Saks et al. showed that an existing protocol by Gallager was indeed optimal by a reduction from a generalized noisy decision tree and produced a \Omega(n\log(n)) lower bound on the depth of the tree that learns the input.

Although information gain is usually a good measure for deciding the relevance of an attribute, it is not perfect. A notable problem occurs when information gain is applied to attributes that can take on a large number of distinct values. For example, suppose that one is building a decision tree for some data describing the customers of a business. Information gain is often used to decide which of the attributes are the most relevant, so they can be tested near the root of the tree.

In a decision tree, all paths from the root node to the leaf node proceed by way of conjunction, or AND. In a decision graph, it is possible to use disjunctions (ORs) to join two more paths together using minimum message length (MML). Decision graphs have been further extended to allow for previously unstated new attributes to be learnt dynamically and used at different places within the graph.Tan & Dowe (2003) The more general coding scheme results in better predictive accuracy and log-loss probabilistic scoring.

British Naturism felt police officers needed to be better informed, and after having discussions with the senior police officer in the College of Policing in April 2018 mutually satisfactory wording was agreed, and the resultant preamble and "decision tree" for dealing with complaints about public nudity has been uploaded to the Police Training manuals. Some laws target naturism. In the U.S. State of Arkansas, nudism is illegal beyond the immediate family unit, even on private property. It is also a crime to "promote" or "advocate" nudism.

From 2010 to 2013 it published an online medical journal, the Journal of Surgical Radiology. It ceased publication despite having claimed to accrue 50,000 subscribers, because Desai "ran out of time." In June 2020 Desai's spokesperson said Surgisphere had 11 employees and had been compiling a global hospital records database since 2008. In its promotional material and press releases, Surgisphere claimed to have a cloud-based healthcare data analytics platform and to be "leveraging... its global research network and advanced machine learning" using decision tree analysis.

In addition to the predefined expression types (decision table, decision tree, formula, database access, loops, etc.) and actions (sending e-mails, triggering a workflow, etc.), BRFplus can be extended by custom expression types. Also, direct calls of function modules as well as ABAP OO class methods are supported so that the entire range of the ABAP programming language is available for solving business tasks. BRFplus comes with an optional versioning mechanism. Versioning can be switched on and off for individual objects as well as for entire applications.

The proposal applies machine learning for anomaly detection, providing energy-efficiency to a Decision Tree, Naive-Bayes, and k-Nearest Neighbors classifiers implementation in an Atom CPU and its hardware-friendly implementation in a FPGA. In the literature, this was the first work that implement each classifier equivalently in software and hardware and measures its energy consumption on both. Additionally, it was the first time that was measured the energy consumption for extracting each features used to make the network packet classification, implemented in software and hardware.

We speak of "contingent planning" when the environment is observable through sensors, which can be faulty. It is thus a situation where the planning agent acts under incomplete information. For a contingent planning problem, a plan is no longer a sequence of actions but a decision tree because each step of the plan is represented by a set of states rather than a single perfectly observable state, as in the case of classical planning. The selected actions depend on the state of the system.

The game is played from a first-person perspective and composed entirely of full motion video scenes as the unnamed protagonist attempts to flee a man wielding an axe. Gameplay involves the player either making choices as regards what action to take at a given moment, or tapping on the touchscreen during context sensitive moments. An example of the player being offered a choice of options as to what to do next. When the player is presented with a choice, the game cuts to a decision tree, where the player selects their choice.

LightGBM, short for Light Gradient Boosted Machine, is a free and open source distributed gradient boosting framework for machine learning originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability. The framework supports different algorithms including GBT, GBDT, GBRT, GBM, MART and RF. LightGBM works on Linux, Windows, and macOS and supports C++, Python, R, and C#. The source code is licensed under MIT License and available on GitHub.

Diagnostic and treatment support systems are typically designed to provide healthcare workers in remote areas advice about diagnosis and treatment of patients. While some projects may provide mobile phone applications—such as a step-by-step medical decision tree systems—to help healthcare workers diagnosis, other projects provide direct diagnosis to patients themselves. In such cases, known as telemedicine, patients might take a photograph of a wound or illness and allow a remote physician diagnose to help treat the medical problem. Both diagnosis and treatment support projects attempt to mitigate the cost and time of travel for patients located in remote areas.

Systematic political science, as developed by Dallas F. Bell Jr., basically is the use of game theory methods to mathematically unify the anthropocentric academic disciplines of theology, epistemology, psychology, sociology and eschatology for computerized analysis and predictions after verification and validation methods are employed, such as red team procedures. The discipline of systematic political science has two natural divisions of emphasis--pure and applied. Pure (systematic) political science focuses on the parameters of individual, institutional, and societal behavior(s). The behavioral parameters create probable decision-tree algorithms from a norm within the min max vector space sum, ∑.

A decision process is thus an intrinsically contextual process, hence it cannot be modeled in a single Kolmogorovian probability space, which justifies the employment of quantum probability models in decision theory. More explicitly, the paradoxical situations above can be represented in a unified Hilbert space formalism where human behavior under uncertainty is explained in terms of genuine quantum aspects, namely, superposition, interference, contextuality and incompatibility. Considering automated decision making, quantum decision trees have different structure compared to classical decision trees. Data can be analyzed to see if a quantum decision tree model fits the data better.

From Smart Homes to Smart Cities: Opportunities and Challenges from an Industrial PerspectiveSmart cities and smart homes: From realization to realityHow smart homes can connect to smart citiesRedefining the smart home in smart citiesHow smart homes can connect to smart cities Bicycle-sharing systems are an important element in smart cities.Bike sharing as a key smart city service Smart mobility is also important to smart citiesSmart mobility in smart cities. Intelligent transportation systems and CCTVUrban Distribution CCTV for Smart City Using Decision Tree Methods systems are also being developed. Some smart cities also have digital libraries.

God in the Age of Science? A Critique of Religious Reason is a 2012 book by the Dutch philosopher Herman Philipse, written in English and published in the United Kingdom. Philipse found his Atheist Manifesto (1995) to be too hastily and superficially written, and decided to set up a more complete work to systematically refute all the arguments for the existence of God and adherence to any form of theism. To gain insight in how a religious person substantiates the existence of God, Philipse presents a "religious decision tree" that leads to four categories of theists.

Thus, the code becomes a large outer loop traversing through the objects, with a large decision tree inside the loop querying the type of the object. Another problem with this approach is that it is very easy to miss a shape in one or more savers, or a new primitive shape is introduced, but the save routine is implemented only for one file type and not others, leading to code extension and maintenance problems. Instead, the visitor pattern can be applied. It encodes a logical operation on the whole hierarchy into one class containing one method per type.

Holberton used a deck of playing cards to develop the decision tree for the binary sort function, and wrote the code to employ a group of ten tape drives to read and write data as needed during the process. She wrote the first statistical analysis package, which was used for the 1950 US Census. In 1953 she was made a supervisor of advanced programming in a part of the Navy’s Applied Math lab in Maryland, where she stayed until 1966. Holberton worked with John Mauchly to develop the C-10 instruction set for BINAC, which is considered to be the prototype of all modern programming languages.

An influence diagram (ID) (also called a relevance diagram, decision diagram or a decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network, in which not only probabilistic inference problems but also decision making problems (following the maximum expected utility criterion) can be modeled and solved. ID was first developed in the mid-1970s by decision analysts with an intuitive semantic that is easy to understand. It is now adopted widely and becoming an alternative to the decision tree which typically suffers from exponential growth in number of branches with each variable modeled.

Shortly after shared computing made its debut in the early 1960s individuals began seeking ways to exploit security vulnerabilities for personal gain. As a result, engineers and computer scientists soon began developing threat modeling concepts for information technology systems. Early IT-based threat modeling methodologies were based on the concept of architectural patterns first presented by Christopher Alexander in 1977. In 1988 Robert Barnard developed and successfully applied the first profile for an IT-system attacker. In 1994, Edward Amoroso put forth the concept of a “threat tree” in his book, “Fundamentals of Computer Security Technology.” The concept of a threat tree was based on decision tree diagrams.

She used the findings concerning etiology to develop the theoretical framework of unmet needs underlying agitated behavior, and to contrast it with alternative theories. The most common unmet needs are needs for meaningful activity, for social contact and for relief from pain and discomfort. This conceptualization became the basis for subsequent studies of non-pharmacological treatment of behavioral symptoms manifested by persons with dementia. For these studies she devised a decision tree algorithm (named Treatment Routes for Exploring Agitation; TREA) in order to assist caregivers in identifying the needs of the person with dementia, and matching the intervention to the unmet need and to the person’s unique preferences and abilities.

EMS providers work under the authority and indirect supervision of a medical director, or board-certified physician who oversees the policies and protocols of a particular EMS system or organization. Both the medical director and the actions he or she undertakes are often referred to as "Medical Control". Equipment and procedures are necessarily limited in the pre-hospital environment, and EMS professionals are trained to follow a formal and carefully designed decision tree (more commonly referred to as a "protocol") which has been approved by Medical Control. This protocol helps ensure a consistent approach to the most common types of emergencies the EMS professional may encounter.

The trace is zero if and only if the graph is triangle-free. For dense graphs, it is more efficient to use this simple algorithm which relies on matrix multiplication, since it gets the time complexity down to O(n2.373), where n is the number of vertices. As showed, triangle-free graph recognition is equivalent in complexity to median graph recognition; however, the current best algorithms for median graph recognition use triangle detection as a subroutine rather than vice versa. The decision tree complexity or query complexity of the problem, where the queries are to an oracle which stores the adjacency matrix of a graph, is Θ(n2).

The color of the root node will determine the nature of the game. The diagram shows a game tree for an arbitrary game, colored using the above algorithm. It is usually possible to solve a game (in this technical sense of "solve") using only a subset of the game tree, since in many games a move need not be analyzed if there is another move that is better for the same player (for example alpha-beta pruning can be used in many deterministic games). Any subtree that can be used to solve the game is known as a decision tree, and the sizes of decision trees of various shapes are used as measures of game complexity.

Litigation risk analysis is a growing practice by lawyers, mediators, and other alternative dispute resolution (ADR) professionals. When applied in mediation settings, litigation risk analysis is used to determine litigated best alternative to negotiated agreement (BATNA) and worst alternative to negotiated agreement (WATNA) scenarios based upon the probabilities and possible outcomes of continuing to litigate the case rather than settle. The process of performing a litigation risk analysis by mediators has been hampered by the need for mediators to physically draw out the decision tree and perform calculations to arrive at an expected value (EV). However, there have been calls for more mediators to adopt the practice of performing such an analysis.

An asymptotic lower bound of Ω(n log n) for time complexity of the EMST problem can be established in restricted models of computation, such as the algebraic decision tree and algebraic computation tree models, in which the algorithm has access to the input points only through certain restricted primitives that perform simple algebraic computations on their coordinates: in these models, the closest pair of points problem requires Ω(n log n) time, but the closest pair is necessarily an edge of the EMST, so the EMST also requires this much time.. However, if the input points have integer coordinates and bitwise operations and table indexing operations are permitted using those coordinates, then faster algorithms are possible.

In the computational complexity theory and quantum computing, Simon's problem is a computational problem that can be solved exponentially faster on a quantum computer than on a classical (or traditional) computer. Although the problem itself is of little practical value, it can be proved that a quantum algorithm can solve this problem exponentially faster than any known classical algorithm. The problem is set in the model of decision tree complexity or query complexity and was conceived by Daniel Simon in 1994. Simon exhibited a quantum algorithm, usually called Simon's algorithm, that solves the problem exponentially faster than any deterministic or probabilistic classical algorithm, requiring exponentially less computation time (or more precisely, queries) than the best classical probabilistic algorithm.

The acronym SAF stands for Simple Analysis of Failure. The SAF2002 model was developed by analyzing the financial data of 1,436 bankrupt companies and 3,434 non-bankrupt companies extracted by a systematic sampling method from 107,034 companies. The variables for the model were selected by using a Classification and Regression Tree (CART) type of decision tree learning approach to analyze the financial data of Japanese companies that entered bankruptcy between 1992 and 2001. The four variables of the model that the CART approach identified are: (x_1) Retained Earnings to Total Liabilities and Owners’ Equity, (x_2) Net Income Before Tax to Total Liabilities and Owners’ Equity, (x_3) Inventory Turnover Period, and (x_4) Interest Expenses to Sales.

In Fisher's (1987) description of COBWEB, the measure he uses to evaluate the quality of the hierarchy is Gluck and Corter's (1985) category utility (CU) measure, which he re-derives in his paper. The motivation for the measure is highly similar to the "information gain" measure introduced by Quinlan for decision tree learning. It has previously been shown that the CU for feature-based classification is the same as the mutual information between the feature variables and the class variable (Gluck & Corter, 1985; Corter & Gluck, 1992), and since this measure is much better known, we proceed here with mutual information as the measure of category "goodness". What we wish to evaluate is the overall utility of grouping the objects into a particular hierarchical categorization structure.

Accordingly, they probed the roles of Olig1 and Olig2 transcription factors in astroglial specification and found that just as Olig1/2 promote motor neuron specification and suppress interneuron specification in the neurogenic phase, Olig1/2 promote oligodendrocyte specification and suppress astroglial specification in the later phase. This work has changed the way scientists think about the “logic” or “decision tree” that governs the differentiation of multipotent neural stems cells into neurons, astrocytes, and oligodendrocytes as they showed that cell subtype is fate restricted prior to restricting the overall neuronal or glial fate. Choi then led some of the first projects in Anderson's lab exploring the neural circuits underlying innate behaviors. All animals possess the innate ability to detect social stimuli of another animal and exhibit either defensive or reproductive behaviors in response.

Users of dialogue mapping have reported that dialogue mapping, under certain conditions, can improve the efficiency of meetings by reducing unnecessary redundancy and digressions in conversations, among other benefits. A dialogue map does not aim to be as formal as, for example, a logic diagram or decision tree, but rather aims to be a comprehensive display of all the ideas that people shared during a conversation. Other decision algorithms can be applied to a dialogue map after it has been created,For example, see section 4.2, "Argument maps as reasoning tools", in: although dialogue mapping is also well suited to situations that are too complex and context- dependent for an algorithmic approach to decision-making. Some researchers and practitioners have combined IBIS with numerical decision-making software based on multi-criteria decision making.

The Shivah has received largely positive reviews. The A.V. Club awarded it a B and stated that "The Shivah fits a compelling moral conscience over a tight decision tree, and compared to sillier interactive fiction like Phoenix Wright: Ace Attorney or Hotel Dusk: Room 215, its rewards are subtler, and more satisfying",Dahlen, Chris (April 16, 2007) The Shivah Review, The A.V. Club, accessed February 20, 2013 while Faithgames stated that it is "not only an excellent indie adventure game, but also one of the best examples of portraying faith through a game that I've ever seen" Shivah Review at Faith Games (cited 18 December 2006) Much of the media coverage focused on the unique choice of a Rabbi as the game's protagonist.Ashcraft, Bryan (December 13, 2006) Talk To People. Punch Them.

Spitzer was a major architect of the modern classification of mental disorders. In 1968, he co-developed a computer program, Diagno I, based on a logical decision tree, that could derive a diagnosis from the scores on a Psychiatric Status Schedule which he co- published in 1970 and that the United States Steering Committee for the United States–United Kingdom Diagnostic Project used to check the consistency of its results. Spitzer was a member on the four-person United States Steering Committee for the United States–United Kingdom Diagnostic Project, which published their results in 1972. They found the most important difference between countries was that the concept of schizophrenia used in New York was much broader than the one used in London, and included patients who would have been termed manic-depressive or bipolar.

Burstall studied physics at the University of Cambridge, then an M.Sc. in operational research at Birmingham University. He worked for three years before returning to Birmingham University to earn a Ph.D. in 1966 with thesis titled Heuristic and Decision Tree Methods on Computers: Some Operational Research Applications under the supervision of N. A. Dudley and K. B. Haley. Burstall was an early and influential proponent of functional programming, pattern matching, and list comprehension, and is known for his work with Robin Popplestone on POP, an innovative programming language developed at Edinburgh around 1970, and later work with John Darlington on NPL and program transformation and with David MacQueen and Don Sannella on Hope, a precursor to Standard ML, Miranda, and Haskell. In 1995, he was elected a Fellow of the Royal Society of Edinburgh.

According to Dejter and Delgado,Dejter I. J.; Delgado A. A. "Perfect domination in rectangular grid graphs", J. Combin. Math. Combin. Comput., 70 (2009), 177-196. given a vertex subset S' of a side Pm of an m x n grid graph G, the perfect dominating sets S in G with S' being the intersection of S with V(Pm) can be determined via an exhaustive algorithm of running time O(2m+n). Extending the algorithm to infinite-grid graphs of width m-1, periodicity makes the binary decision tree prunable into a finite threaded tree, a closed walk of which yields all such sets S. The graphs induced by the complements of such sets S can be codified by arrays of ordered pairs of positive integers, for the growth and determination of which a speedier algorithm exists.

In the study of decision-making, including the disciplines of psychology, artificial intelligence, and management science, a fast-and-frugal tree is a type of classification tree or decision tree. As shown in Figure 1--which will be explained in detail later--fast-and-frugal trees are simple graphical structures that ask one question at a time. The goal is to classify an object (in Figure 1: a patient suspected of heart disease) into a category for the purpose of making a decision (in Figure 1 there are two possibilities, patient assigned to a regular nursing bed or to emergency care). Unlike other classification and decision trees, such as Leo Breiman's CART, fast-and-frugal trees have been defined to be intentionally simple, both in their construction as well as their execution, and operate speedily with little information.

An EMST of 25 random points in the plane The Euclidean minimum spanning tree or EMST is a minimum spanning tree of a set of n points in the plane (or more generally in ℝd), where the weight of the edge between each pair of points is the Euclidean distance between those two points. In simpler terms, an EMST connects a set of dots using lines such that the total length of all the lines is minimized and any dot can be reached from any other by following the lines. In the plane, an EMST for a given set of points may be found in Θ(n log n) time using O(n) space in the algebraic decision tree model of computation. Faster randomized algorithms of complexity O(n log log n) are known in more powerful models of computation that more accurately model the abilities of real computers.. In higher dimensions (d ≥ 3), finding an optimal algorithm remains an open problem.

British Naturism has sought legal and political protection against discrimination for naturists in the United Kingdom, where an opinion poll in 2008 estimated the number of people describing themselves as naturist or nudist at 3.7 million. It also runs public facing campaigns, including Women in Naturism, which encourages women to try naturist activity, Bare all for polar bears which seeks to raise money for environmental conservation and The Great British Skinny Dip, which encourages costume free swimming events to be run, not just by naturist clubs, but also public pools, spas, lidos and natural settings such as lakes. In April 2018, British Naturism announced that in discussion with the senior officer at the police college a mutually satisfactory solution was reached, and the resultant preamble and "decision tree" for dealing with complaints about public nudity has been uploaded to the Police Training manuals. Naturism is protected as a philosophical belief by the Equalities Act of 2010 and section 66 of the 2003 Sexual Offences Act.

Before the term fast-and-frugal trees was coined in 2003, these models of heuristics had been used in several contexts without having been explicitly conceptualized or defined as such . In tasks where a binary decision or classification needs to be made (e.g., a doctor has to decide whether to assign a patient with severe chest pain to the coronary care unit or to a regular nursing bed) and there are m cues (this is the terminology used in psychology for what is called features in artificial intelligence and attributes in management science), available for making such a decision, an FFT is defined as follows: A fast- and-frugal tree is a decision tree that has m+1 exits, with one exit for each of the first m -1 cues and two exits for the last cue. Mathematically, fast- and-frugal trees can be viewed as lexicographic heuristics or as linear models with non-compensatory weights as proven by Martignon, Katsikopoulos and Woike in 2008 .

Automation technology may be used in the production of legal documents, such as employment contracts and estate planning documents, potentially with the use of an online interface or decision tree. In large law firms document assembly systems are often used to systemize work, such as through the creation of complex term sheets and the first drafts of credit agreements. With the liberalisation of the UK legal services market spearheaded by the Legal Services Act 2007 large institutions have broadened their services to include legal assistance for their customers.Article: Braced for the big bang and Tesco law Jon Robins The TimesArticle: A trip to the shops can end in divorce Ellen Kelleher The Financial Times Most of these companies use some element of document automation technology to provide legal document services over the Web.Article: Why big brand legal services are bad news for solicitors Neil Rose The Guardian (Tuesday 2 November 2010) This has been seen as heralding a trend towards commoditisation whereby technologies like document automation result in high volume, low margin legal services being ‘packaged’ and provided to a mass-market audience.