Binary classification is one of the types of classification problems in machine learning where we have to classify between two mutually exclusive classes. For example, classifying messages as spam or not spam, classifying news as Fake or Real. There are many classification algorithms in machine learning, but not all of them can be used for binary classification. So if you want to know about the best binary classification algorithms in machine learning, this article is for you. In this article, I will introduce you to some of the best binary classification algorithms in machine learning that you should prefer while working on binary classification problems.
Binary Classification Algorithms
When you need to train a machine learning model to classify between two classes, this is the problem of binary classification. This is one of the simplest classification problems in machine learning. Not all classification algorithms can be used to solve binary classification problems, but some algorithms adapt well to all types of classification problems. So here are some of the best binary classification algorithms you should know:
- Bernoulli Naive Bayes: The Bernoulli Naïve Bayes is one of the variations of the Naïve Bayes algorithm that is very useful in a binary classification problem. Some of the advantages of using this algorithm for binary classification are:
- it is very fast as compared to the other classification algorithms
- it can perform well in both large and small datasets
- it can also handle irrelevant features easily.
- Logistic Regression: Despite its name, Logistic Regression is a very simple classification algorithm and one of the most efficient machine learning classifiers for binary classification problems. Some of the advantages of logistic regression for binary classification are it is easier to implement and very efficient to train. One disadvantage this algorithm has is that it is affected by outliers.
- Decision Tree Classifier: If your dataset contains outliers, you need a powerful algorithm that can’t be affected by outliers. The decision tree algorithm is such a powerful algorithm that it can be used in any type of classification task without being affected by outliers. One of the advantages of the decision tree classification algorithm is that it requires very little data preparation because it is not affected by outliers.
So Bernoulli Naïve Bayes, Logistic Regression, and Decision tree are some of the algorithms that you should always prefer while working on a binary classification problem.
When you need to train a machine learning model to classify between two classes then this is the problem of binary classification. It is one of the simplest classification problems in machine learning. I hope you liked this article on the best machine learning algorithms for binary classification. Feel free to ask your valuable questions in the comments section below.