Building an effective and accurate classifier
Classification in machine learning is a supervised learning task that involves categorizing or classifying data into predefined classes or categories. It is one of the fundamental and widely used techniques in machine learning and data mining. The goal of classification is to develop a model or classifier that can accurately assign new, unseen instances to the correct class based on their features or attributes. The classifier learns patterns and relationships from a labeled training dataset, where each instance is associated with a known class label.
We will first discuss SVMs.
SVMs explained
SVMs are powerful supervised machine learning algorithms used for classification and regression tasks. They are particularly effective in solving complex problems with a clear margin of separation between classes. SVMs can handle both linearly separable and non-linearly separable data by transforming the input space into a higher-dimensional...
