Binary classification

See also: Machine learning terms

Introduction

Binary classification is a type of machine learning problem where the goal is to classify input data into one of two classes or categories. The two classes are often labeled as positive (1) and negative (0), or as true (1) and false (0).

What is Binary Classification?

In binary classification, a machine learning algorithm learns to classify input data into one of two categories based on a set of labeled training data. The algorithm receives input data and makes a prediction about which class the input belongs to.

The two classes in binary classification can represent various things, such as spam or not spam, fraud or not fraud, and disease or not disease. The goal is to correctly classify new input data into the appropriate class, based on the patterns learned from the training data.

Binary classification is a supervised learning task, meaning that the algorithm is trained using labeled data, where each data point is associated with a label that indicates the class it belongs to.

How is Binary Classification Done?

Binary classification is typically done using a machine learning algorithm that learns from a set of labeled training data. The algorithm receives input data and makes a prediction about which class the input belongs to.

The performance of the algorithm is evaluated on a set of test data that is separate from the training data. The test data is also labeled, and the performance of the algorithm is measured by comparing the predicted labels to the true labels of the test data.

There are various machine learning algorithms used for binary classification, including logistic regression, decision trees, random forests, and support vector machines (SVM). The choice of algorithm depends on the specific problem being solved and the characteristics of the data.

Evaluation Metrics for Binary Classification

The performance of a binary classification model is evaluated using various metrics, including accuracy, precision, recall, and F1 score.

Accuracy measures the proportion of correct predictions made by the model on a set of test data. Precision measures the proportion of true positive predictions among all the positive predictions made by the model. Recall measures the proportion of true positive predictions among all the actual positive samples in the test data. The F1 score is the harmonic mean of precision and recall.

The choice of the appropriate evaluation metric depends on the specific problem and the relative importance of correctly identifying each class.

Explain Like I'm 5 (ELI5)

Binary classification is like a game of guessing whether something is true or false. For example, a computer program can learn to guess whether an email is spam or not spam. The program is taught by looking at lots of emails and seeing which ones are spam and which ones are not. Then, when it sees a new email, it tries to guess whether it's spam or not based on what it learned before. The program gets points for guessing correctly, and it can get better at the game by looking at more emails.