support vector machines
Support Vector Machines (SVM) are a type of machine learning algorithm used for classification tasks. Imagine you have two groups of data points on a graph. SVM finds the best line (or hyperplane in higher dimensions) that separates these groups with the widest possible gap. This "gap" is determined by the support vectors, which are the closest points from each group. By maximizing this margin, SVM ensures better generalization to new, unseen data. It's widely used in various applications, from image recognition to text categorization, due to its effectiveness in handling complex datasets.
Additional Insights
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Support Vector Machines (SVM) are a type of machine learning algorithm used for classification tasks. Imagine drawing a line between two groups of points on a graph. An SVM finds the best line (or boundary) that separates these groups while maximizing the distance between the closest points of each group. This helps the model make accurate predictions for new, unseen data. SVMs are effective in high-dimensional spaces and can be used in applications like image recognition and spam detection, demonstrating their versatility in handling complex data scenarios.