When preparing a dataset to train a machine learning model with binary outcomes, what model type should be used?

The selection of a binary classifier model is appropriate when preparing a dataset to train a machine learning model with binary outcomes. Binary outcomes refer to scenarios where there are only two possible results, such as true/false, yes/no, or 0/1 classifications. A binary classifier is specifically designed to distinguish between these two classes.

Binary classifiers employ various algorithms, including logistic regression, support vector machines, decision trees, and neural networks, to make predictions about which class an observation fits into based on the input features. They are trained using labeled data, where the outcomes are known, allowing the model to learn the decision boundary that separates the two classes.

In contrast, clustering models are used for unsupervised learning and focus on grouping similar data points together without predefined labels, making them unsuitable for binary outcome tasks. Sentiment analysis, while useful for classifying sentiment in text, typically involves multi-class problems (positive, negative, neutral) rather than binary outcomes. Lastly, regression models are primarily used for predicting continuous numerical values rather than categorical outcomes, which further solidifies the binary classifier’s role as the correct model type for this specific task.