In the classification problems, there are two types of learners: Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier.Įxample: Classifications of types of crops, Classification of types of music.Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier.Įxamples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc.The algorithm which implements the classification on a dataset is known as a classifier. These classes have features that are similar to each other and dissimilar to other classes. In the below diagram, there are two classes, class A and Class B. The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.Ĭlassification algorithms can be better understood using the below diagram. The best example of an ML classification algorithm is Email Spam Detector. In classification algorithm, a discrete output function(y) is mapped to input variable(x). Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.

Unlike regression, the output variable of Classification is a category, not a value, such as 'Green or Blue', 'fruit or animal', etc. Classes can be called as targets/labels or categories. Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms. Next → ← prev Classification Algorithm in Machine LearningĪs we know, the Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms.

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