Some of the most popular machine learning algorithms for creating text classifi ion models include the Naive Bayes family of algorithms support vector machines SVM and deep learning.
Classifi ion can be performed on structured or unstructured data. Classifi ion is a technique where we egorize data into a given number of classes. The main goal of a classifi ion problem is to identify the egory/class to which a new data will fall under. Few of the terminologies encountered in machine learning – classifi ion:
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Naïve Bayes Classifier is amongst the most popular learning method grouped by similarities that works on the popular Bayes Theorem of Probability- to build machine learning models particularly for disease prediction and document classifi ion.
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kNN or k-Nearest Neighbors is one of the most popular machine learning classifi ion algorithms. It stores all of the available examples and then classifies the new ones based on similarities in distance metrics. It belongs to instance-based and lazy learning systems.
Classifi ion is technique to egorize our data into a desired and distinct number of classes where we can assign label to each class. Appli ions of Classifi ion are: speech recognition…