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Data Structure Articles
Page 131 of 164
What are the characteristics of Decision tree induction?
There are various characteristics of decision tree induction is as follows −Decision tree induction is a nonparametric method for constructing classification models. In other terms, it does not need some previous assumptions regarding the type of probability distributions satisfied by the class and the different attributes.It can be finding an optimal decision tree is an NP-complete problem. Many decision tree algorithms employ a heuristic-based approach to guide their search in the vast hypothesis space.There are various techniques developed for constructing computationally inexpensive decision trees, making it possible to quickly construct models even when the training set size is very large. ...
Read MoreWhat are the methods for expressing attribute test conditions?
Decision tree induction is the learning of decision trees from class-labeled training tuples. A decision tree is a sequential diagram-like tree structure, where every internal node (non-leaf node) indicates a test on an attribute, each branch defines a result of the test, and each leaf node (or terminal node) influences a class label. The largest node in a tree is the root node.Decision tree induction generates a flowchart-like structure where each internal (non-leaf) node indicates a test on an attribute, each branch corresponds to a result of the test, and each external (leaf) node indicates a class prediction.At each node, ...
Read MoreWhat is Variable Transformation?
A variable transformation defines a transformation that is used to some values of a variable. In other terms, for every object, the revolution is used to the value of the variable for that object. For instance, if only the significance of a variable is essential, then the values of the variable can be changed by creating the absolute value.There are two types of variable transformations: simple functional transformations and normalization.Simple FunctionsA simple mathematical function is used to each value independently. If r is a variable, then examples of such transformations include xk, logx, ex, $\sqrt{x}$, $\frac{1}{x}$, sinx, or |x|. In ...
Read MoreWhat are the types of data mining models?
Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.Data mining techniques can be used to make three kinds of models for three kinds of tasks such as descriptive profiling, directed profiling, and prediction.Descriptive Profiling − Descriptive models defines what is in the record. The output is multiple ...
Read MoreWhat is Hypothesis Testing?
Hypothesis testing is the simplest approach to integrating data into a company’s decision-making processes. The purpose of hypothesis testing is to substantiate or disprove preconceived ideas, and it is a part of almost all data mining endeavors.Data miners provide bounce back and forth among methods, first thinking up possible descriptions for observed behavior and letting those hypotheses dictate the data be computed.Hypothesis testing is what scientists and statisticians traditionally spend their lives doing. A hypothesis is a proposed explanation whose validity can be tested by analyzing data. Such information can easily be collected by observation or created through an experiment, ...
Read MoreWhat are Single-Attribute Evaluators in data mining?
In single-attribute evaluators, it can be utilized with the Ranker search methods to make a ranked list from which ranker discards a given number. It is also used in the RankSearch method.Relief Attribute Eval is instance-based − It samples instances randomly and checks neighboring instances of the equal and multiple classes. It works on discrete and continuous class data. Parameters define the multiple instances to sample, the various neighbors to check, whether to weight neighbors by distance, and an exponential function that conducts how increasingly weights decay with distance.InfoGain Attribute Eval − It computes attributes by calculating their information gain ...
Read MoreWhat is Bias–Variance Decomposition?
The effect of joining multiple hypotheses can be checked through a theoretical device called the bias-variance decomposition. Suppose it can have an infinite number of separate training sets of similar size and use them to create an infinite number of classifiers.A test instance is treated by all classifiers, and an individual answer is decided by bulk vote. In this situation, errors will appear because no learning design is perfect. The error rate will be based on how well the machine learning approaches connect the problem at hand, and there is also the effect of noise in the record, which cannot ...
Read MoreWhat is Outlier Detection?
An outlier is a data object that diverges essentially from the rest of the objects as if it were produced by several mechanisms. For the content of the demonstration, it can define data objects that are not outliers as “normal” or expected data. Usually, it can define outliers as “abnormal” data.Outliers are data components that cannot be combined in a given class or cluster. These are the data objects which have several behavior from the usual behavior of different data objects. The analysis of this kind of data can be important to mine the knowledge.Outliers are fascinating because they are ...
Read MoreWhat are the approaches of Unsupervised Discretization?
An attribute is discrete if it has an associatively small (finite) number of possible values while a continuous attribute is treated to have a huge number of possible values (infinite).In other term, a discrete data attribute can be viewed as a function whose range is a finite group while a continuous data attribute is a function whose range is an infinite completely ordered group, generally an interval.Discretization aims to decrease the number of possible values a continuous attribute takes by partitioning them into several intervals. There are two methods to the problem of discretization. One is to quantize every attribute ...
Read MoreWhat are Generalizing Exemplars?
Generalized exemplars are the rectangular scope of instance area, known as hyperrectangles because they are high-dimensional. When defining new instances it is essential to convert the distance function to enable the distance to a hyperrectangle to be computed.When a new exemplar is defined correctly, it is generalized by directly merging it with the nearest exemplar of a similar class. The nearest exemplar can be an individual instance or a hyperrectangle.In this method, a new hyperrectangle is generated that covers the previous and the new instance. The hyperrectangle is expanded to surround the new instance. Lastly, if the prediction is false ...
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