In Machine Learning, binary classification consists of distinguishing two classes. For example, classifying messages as spam or not as spam. We have a bunch of machine learning algorithms for the binary classification task, so to help you choose the best algorithm, in this article I will introduce you to the best algorithm for binary classification in machine learning.
What is Binary Classification?
In Machine Learning, binary classification is the task of classifying the data into two classes. Real-world examples of binary classification include problems like finding the best class of customers from two groups for marketing the launch of a product. So, in binary classification, we want to classify the samples into two groups. If we are classifying the samples into more than two classes then it becomes the problem of multiclass classification.
To understand which is the best machine learning algorithm for the task of binary classification you have to go through the implementation and assumptions of all the classification algorithms to get an idea where you should use which algorithm. But to make things simple for you, in the section below, I will introduce you to the best algorithm for binary classification.
Best Algorithm for Binary Classification
There are a bunch of machine learning algorithms that we can use for the task of binary classification. But according to the projects that I have worked on, I have found that the best algorithm for binary classification is dependent on the type of problem you are working on and the kind of data you are using. So below are the best algorithms for the task of binary classification according to the problem you are working on:
- If you are working on a textual dataset where the data is not very large then it is good to use the logistic regression algorithm.
- If you are working on a large dataset of images then you have to use a very powerful classification algorithm. So in this case you can use the Stochastic Gradient Descent Classifier.
- If you are working on a binary classification problem where the data arrives in a continuous flow, in this case, you can use the passive-aggressive classification algorithm.
Whenever you work on a new kind of binary classification problem use as many algorithms that you can to solve that problem. It will help you to decide which algorithm is best to choose to solve the same problem again. I hope you liked this article on the best algorithms for binary classification in machine learning. Feel free to ask your valuable questions in the comments section below.