Supervised learning is a machine learning approach in which algorithms are trained on labelled datasets—that is, data that already includes the correct outputs or classifications. The model learns to ...

Spend a weekend figuring out how to use the ChatGPT API, or sink 80 hours into a digital marketing course: It's your call ...

Gradient Boosting-Based Simultaneous Classification and Regression Approach Abstract: The recent development of advanced data analytics and machine learning promoted the introduction of diverse ...

Supervised classification uses random forest (RF), support vector machine (SVM), classification and regression trees (CARTs), gradient boosting trees (GBTs), and naïve Bayes. An accuracy assessment ...

Abstract The accurate and early detection of coronary heart disease (CHD) is crucial for reducing mortality rates. This study evaluates the predictive performance of three machine learning ...

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of ...

This paper proposes a supervised learning method for SNNs based on associative learning: ALSA. The method is based on the associative learning mechanism, and its realization is similar to the animal ...

Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier ...