docs/_docs/machine-learning/binary-classification/introduction.adoc - ignite - Git at Google

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 = Introduction

 In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

 All existing training algorithms presented in this section are designed to solve binary classification tasks:


 *  Linear SVM (Support Vector Machines)
 *  Decision Trees
 *    Multilayer perceptron
 *    Logistic Regression
 *    k-NN Classification
 *    ANN (Approximate Nearest Neighbor)
 *    Naive Bayes


 Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule.
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	= Introduction

	In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

	All existing training algorithms presented in this section are designed to solve binary classification tasks:


	* Linear SVM (Support Vector Machines)
	* Decision Trees
	* Multilayer perceptron
	* Logistic Regression
	* k-NN Classification
	* ANN (Approximate Nearest Neighbor)
	* Naive Bayes


	Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule.