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The naıve Bayesian classifier creates the assumption of class conditional independence, i.e., given the class label of a tuple, the values of the attributes are considered to be conditionally separate from one another. This defines evaluations.When the assumption affects true, hence the naïve Bayesian classifier is effective in contrast with multiple classifiers. It can represent joint conditional probability distributions.They enable class conditional independencies to be represented among subsets of variables. They support a graphical structure of causal relationships, on which learning can be implemented. Trained Bayesian belief networks are used for classification. Bayesian belief networks are also called belief networks, ... Read More
Bayesian classifiers are statistical classifiers. It can predict class membership probabilities, such as the probability that a given sample applied to a definite class. Bayesian classifiers have also displayed large efficiency and speed when they can have high databases.Because classes are defined, the system must infer rules that supervise the classification, hence the system must be able to discover the description of each class. The descriptions must define the predicting attributes of the training set so that only the positive instances must satisfy the description, not the negative instances. A rule is said to be correct if its description covers ... Read More
The Nearest Neighbour rule produces frequently high performance, without previous assumptions about the allocation from which the training instances are drawn. It includes a training set of both positive and negative cases. A new sample is defined by computing the distance to the convenient training case; the sign of that point then decides the classification of the sample.The k-NN classifier boosts this concept by taking the k nearest points and creating the sign of the majority. It is frequent to choose k small and odd to divide ties (generally 1, 3, or 5). Larger k values help decrease the effects ... Read More
It is a widely used rule induction algorithm called RIPPER. This algorithm scales almost linearly with the several training instances and is especially suited for constructing models from data sets with overloaded class distributions. RIPPER also works well with noisy data sets because it uses a validation set to prevent model overfitting.RIPPER selects the majority class as its default class and understands the rules for identifying the minority class. For multiclass problems, the classes are series as per their frequencies.Let (y1 y2...yc) be the ordered classes, where y1is the least frequent class and yc is the most frequent class. During ... Read More
In this problem, we are given an unordered array arr[] of size N containing values from 1 to N-1 with one value occuring twice in the array. Our task is to find the only repetitive element between 1 to n-1.Let’s take an example to understand the problem, Inputarr[] = {3, 5, 4, 1, 2, 1}Output1Solution ApproachA simple solution to the problem is traversing the array and for each value find whether the element exists somewhere else in the array. Return the value with double occurrence.Example 1Program to illustrate the working of our solution#include using namespace std; int findRepValArr(int arr[], ... Read More
There are several methods for estimating the generalization error of a model during training. The estimated error supports the learning algorithm to do model choice; i.e., to discover a model of the right complexity that is not affected by overfitting.Because the model has been constructed, it can be used in the test set to forecast the class labels of earlier unseen data. It is often useful to measure the performance of the model on the test set because such a measure provides an unbiased estimate of its generalization error. The accuracy or error rate evaluated from the test set can ... Read More
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 More
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 More
In this problem, we are given an arr[] of size N containing values from 1 to N-1 with one value occuring twice in the array. Our task is to find the only repeating element in a sorted array of size n.Let’s take an example to understand the problem, Inputarr[] = {1, 2, 3, 4, 5, 5, 6, 7}Output5Solution ApproachA simple approach to solve the problem is by using linear search and checking if arr[i] and arr[i+1] have the same value. In this case, return arr[i] which is the value repeated.Example 1Program to illustrate the working of our solution#include using ... Read More
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 More