Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Supervised machine learning problems are further divided into classification (predicting non-numeric answers, such as the probability of a missed mortgage payment) and regression (predicting ...

We apply clustering and machine learning techniques to analyze validation reports. The XGB oost model outperforms Logistic regression and clustering methods in predicting dimensions of findings from ...

Compared to other classification techniques, k-NN is easy to implement, supports numeric and categorical predictor variables, and is highly interpretable. By James McCaffrey; 10/01/2024; Multi-class ...

Big Blue's quantum team has mathematically demonstrated that a quantum algorithm could work better than a classical one for machine-learning classification problems. Written by Daphne Leprince ...