Random Forests are an ensemble learning method widely used in artificial intelligence (AI) for classification and regression tasks. This technique builds upon the concept of decision trees, combining the predictions of multiple trees to enhance accuracy and robustness. By leveraging the strengths of individual decision trees while mitigating their weaknesses, Random Forests have become a Read More …
Tag: Artificial Intelligence
Decision Trees in Artificial Intelligence
Decision trees are a popular and intuitive method used in artificial intelligence (AI) for classification and regression tasks. They represent a model in the form of a tree-like structure, where each internal node corresponds to a decision based on an input feature, each branch represents the outcome of that decision, and each leaf node signifies Read More …
SVM – Support Vector Machines
Support Vector Machines (SVMs) are a powerful supervised learning algorithm used in artificial intelligence (AI) for classification and regression tasks. Developed in the 1990s by Vladimir Vapnik and his colleagues, SVMs are particularly effective in high-dimensional spaces and are known for their robustness in handling both linear and non-linear data. How SVMs Work The primary Read More …
Linear Regression in Artificial Intelligence
Linear regression is a fundamental statistical method used in artificial intelligence (AI) and machine learning for modeling the relationship between a dependent variable and one or more independent variables. It is particularly useful for predictive analytics, where the goal is to forecast outcomes based on input data. At its core, linear regression aims to find the Read More …