What is Decision Boundary? Decision Boundary Explained.
A decision boundary is a conceptual boundary or surface that separates different classes or categories in a classification problem. In machine learning, decision boundaries are used to make predictions or classify new instances based on their features.
In a binary classification problem, where there are two classes, the decision boundary is a line, curve, or higher-dimensional surface that separates the instances belonging to different classes. The decision boundary is determined by the algorithm used for classification and is learned from the training data.
For example, consider a simple binary classification problem where we want to predict whether an email is spam or not based on its features such as the sender, subject, and content. The decision boundary could be a straight line in a two-dimensional feature space, where all the points on one side of the line are classified as spam and all the points on the other side are classified as non-spam.
In more complex problems with higher-dimensional feature spaces, the decision boundary can be a more complex surface, such as a curved line or a hyperplane. For instance, in image classification tasks, the decision boundary could be a complex boundary that separates different objects or classes in the image space.
It’s important to note that different machine learning algorithms may have different decision boundaries, and the choice of algorithm can significantly affect the decision boundary and the classification performance. Some algorithms, such as logistic regression, support vector machines (SVMs), or decision trees, may produce linear decision boundaries, while others like k-nearest neighbors (KNN), neural networks, or support vector machines with non-linear kernels can learn more complex decision boundaries.
Understanding the decision boundary is crucial for assessing the performance and limitations of a classification model. It helps in understanding how the model separates different classes and how it may generalize to new, unseen instances. Visualizing the decision boundary can provide insights into the model’s behavior and assist in identifying regions of uncertainty or potential misclassifications.
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