There are two major categories of problems that are often solved by machine learning: regression and classification. Regression is for numeric data (e.g. What is the likely income for someone with ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Budoen, A. , Zhang, M. and Jr., L. (2025) A Comparative Study of Ensemble Learning Techniques and Classification Models to Identify Phishing Websites. Open Access Library Journal, 12, 1-22. doi: ...

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems ...

While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

Linear regression is a simple machine learning algorithm that has many ... k-nearest neighbor, naive Bayes classification, and decision trees. The process can get a bit convoluted at times ...

It's mostly useful to provide a baseline result for comparison with more powerful ML techniques such as logistic regression and k-nearest neighbors. Perceptron classification is arguably the most ...