What if plants could speak when they were thirsty? Agriculture, in essence, is a dialog among crops, soil and climate. Yet ...

This project explores two different investigation using methods of machine learning and hybrid approach to predict the peak energy consumption in Ireland and energy production in Portugal based on ...

In this study, we attempt to leverage the ability of supervised learning methods, such as ANNs, KANs, and gradient-boosted decision trees, to approximate complex multivariate functions in order to ...

Decision tree regression is a fundamental technique that can be used by itself, and is also the basis for powerful ensemble techniques (a collection of many decision trees), notably, AdaBoost ...

Decision trees are a popular machine learning algorithm that can be used for both classification and regression tasks. They operate by recursively dividing the dataset into subsets according to the ...

Each technique has pros and cons. This article explains how to implement decision tree regression from scratch, using the C# language. Compared to other regression techniques, decision tree regression ...

Classification and Regression decision-Tree (CART) algorithm for multiclass classification of PCNSLs, GBMs and METs The CART decision-tree model successfully classified the 3 tumor types in our cohort ...

Decision tree as the name suggests are tree structured predictive model. They are one of the most powerful tools in data mining and machine learning, as it is very easy for a person to derive ...