The model is trained on the basis of millions of emails on different parameters, and whenever it receives a new email, it identifies whether the email is spam or not. The task of the classification algorithm is to find the mapping function to map the input(x) to the discrete output(y).Įxample: The best example to understand the Classification problem is Email Spam Detection. In Classification, a computer program is trained on the training dataset and based on that training, it categorizes the data into different classes. and Classification algorithms are used to predict/Classify the discrete values such as Male or Female, True or False, Spam or Not Spam, etc.Ĭonsider the below diagram: Classification:Ĭlassification is a process of finding a function which helps in dividing the dataset into classes based on different parameters. The main difference between Regression and Classification algorithms that Regression algorithms are used to predict the continuous values such as price, salary, age, etc. But the difference between both is how they are used for different machine learning problems. Both the algorithms are used for prediction in Machine learning and work with the labeled datasets. Regression and Classification algorithms are Supervised Learning algorithms.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |