What is classification in machine learning?

Question

Can anyone explain classification in machine learning?

Answer 1

Classification in Machine Learning is a type of supervised learning that involves classifying the new examples based on observed patterns from the previous data. In simple words, if the target variable is discrete then it is a classification problem.

I have listed a few most used classification algorithms in machine-learning:

• Logistic Regression
• Decision trees
• Random forest
• XG boost

Examples of classification in machine learning include:

• Classification of emails as spam or not spam
• Classifying fraudulent and non-fraudulent transactions

If you are interested to learn Machine learning, I would recommend this Machine learning course by Intellipaat.

You can watch this video to know about classification in machine learning:

Answer 2

Classification is a type of supervised learning where an algorithm or a model determines the data elements and categorizes them into classes. Classification is used everywhere—email spam detection, bank loan approval, sales funnel customer classification, and more. There are two types of classification algorithms in Machine Learning. They are linear and nonlinear models, also called 'lazy learners' and 'eager learners,' respectively. Logistic regression and support vector machines come under linear models, and models such as Naive Bayes, decision trees, random forests, and KNNs come under non-linear models. The goal is to perform analysis on the input data to categorize it into classes by using mapping functions and approximation to determine the output variables at every instance.

If you are looking for an online course to learn Machine Learning, I recommend this Machine Learning Online Course by Intellipaat.