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What is Classification?

Classification refers to the organization of data into categories

. Individual data items are represented as “objects” which belong to a class. Each data item is then conceptually assigned to one and only one type. A classification algorithm derives the classes from the given input data.

Classification tasks are used in many applications, including bioinformatics, text mining (pdf), software bug detection, computer security, and economics.

Classification algorithms are often supervised because they require an external set of “ground truth” data, which is used to evaluate the performance of the classification algorithm. Classification tasks can also be unsupervised when there is no ground truth information available. They then must still be assessed based on some measure of quality.

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Classification of Data

Private data: private data refers to data only available to the person or company who collected it.

Public data: public data refers to publicly available data on governmental websites such as the census bureau, social media sites (such as Facebook), and various other statistics sources.

Restricted data: restricted data refers to publicly available data on governmental websites, but the specific information can be restricted.

Techniques Covered in Our Classification Assignment Help

K-Nearest Neighbor Classification

It refers to a family of machine learning algorithms that make predictions by finding the members of a training set (or other data source) that are most similar to a query point.

Naïve Bayes Classification

It is a simple technique for classifying text documents based on applying Bayes’ theorem with strong (naïve) independence assumptions. The model’s training is based upon whether each word in the paper appears more or less often in one class than in the other, and it was found to perform well even on extensive sets of data with many classes.

ANOVA

In statistics, analysis of variance (ANOVA) follows an experimental design that tests, for example, how different groups of people or things respond to other treatments. ANOVA makes simpler alternative analyses such as Student’s t-test possible. Sir Ronald Fisher developed the fundamental idea from 1919 onwards, but he did not publish his discoveries until 1925, when he popularized it through influential books. It offers an optimal solution to a problem encountered in the design of experiments by taking into account several sources of experimental error or variation simultaneously. It is widely used in agriculture, medical statistics, engineering, marketing research, psychology and other areas.

SVM

Support vector machines (SVMs) are supervised learning models with associated learning algorithms analyzing data used for classification and regression analysis.

The most popular types of SVM are based on either structural risk minimization (SRM), also known as maximum margin classification or empirical risk minimization (ERM), also known as maximum likelihood classification. The third type of SVM model called relevance vectors has also been proposed, but its efficiency and effectiveness are still under investigation.

Cross-validation loss

This parameter is used in machine learning algorithms that define the loss function to be optimized during training. It represents the expected error of estimators computed from test sets using training sets (also called validation set).

Linear discriminant

Linear Discriminant Analysis is a statistical technique for predictive modelling that allows classifying observations into two or more predefined classes. It extends the logic used in discriminant analysis which allows only two types but treats both variables as continuous. Fisher introduced linear discriminant function (LDF) in 1936, and it has been widely applied in machine learning.

LTSA

Locality-sensitive hashing (LSH) is a method for facilitating an approximate nearest neighbour search by an algorithm such as k-nearest neighbor with high accuracy and small error, even in the presence of significant noise in the data. LS-TSA can perform tasks ranging from large scale image similarity retrieval to fast indexing schemes for vast amounts of data. Its variants have been used in many diverse applications ranging from high energy physics to medical image retrieval, bioinformatics and computer vision.

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