naive bayes

Naive Bayes : The Definitive Guide

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Naive Bayes is a probabilistic classifier which is based on Bayes theorem. Bayes theorem provides a way of calculating the posterior probability. Naive Bayes classifier assumes that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence. Naive Bayes is used for choosing the latest SEO techniques. Bayesian probability: Posterior = prior x likelihood/evidence Imported library: […]

rocchio algorithm

Rocchio Algorithm : The Definitive Guide

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Rocchio algorithm is based on a method of relevance feedback. The Rocchio algorithm incorporates relevance feedback information into the vector space model, the Rocchio feedback approach was developed using the Vector Space Model. This algorithm has a general conception of which documents should be denoted as relevant or non-relevant. Therefore, the search query includes an arbitrary percentage of relevant and non-relevant documents as a means of increasing the search engine’s recall, and possibly the precision […]

Cosine Similarity

Cosine Similarity : The Definitive Guide

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Cosine similarity is a similarity measurement between two non-zero vectors that measures the cosine of the angle between them which is very useful for an SEO company. The two vectors with the same orientation have a cosine similarity of 1 and also with different orientation the cosine similarity will be 0 or in between 0-1. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in [ 0, 1]. The […]


Hierarchical Clustering : The Definitive Guide

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Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other. This is used in SEO consultant service. Strategies for hierarchical clustering generally fall into two types: Agglomerative: This is a “bottom-up” approach: each observation starts in its own cluster, and pairs of clusters […]

LDA topic modeling

Topic Modelling : The Definitive Guide

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Topic models provide a simple way to analyze large volumes of unlabeled text. Topic modeling is a method for unsupervised classification of documents. Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words. This allows documents to “overlap” each other in terms of content, rather than being separated into discrete groups, in a way […]