Binary classification is a type of supervised learning task in machine learning where the goal is to classify input instances into one of two possible classes or categories. In other words, it involves predicting a binary outcome, where the target variable can take on only two values, typically represented as 0 and 1 (or “negative” and “positive”, “no” and “yes”, etc.).

Some common use cases of binary classification include:

1.Email Spam Detection: Classifying emails as either spam (1) or not spam (0).

2. Sentiment Analysis: Classifying text or social media posts as expressing positive (1) or negative (0) sentiment.

3. Fraud Detection: Identifying fraudulent transactions as either fraudulent (1) or legitimate (0).

Common algorithms used for binary classification include logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors (KNN). Neural nets can also be used in case with complex dataset with high number of features or classifications that require higher performance.