Description: Instance-Based Classification is a machine learning approach that classifies new instances based on their similarity to previously known instances. This method focuses on the idea that similar instances tend to belong to the same class. Unlike other classification algorithms that create a general model from training data, instance-based classification stores all training examples and performs classification at runtime. This allows the algorithm to be highly flexible and adaptive, as it can adjust to new instances without the need to retrain a model. Key features of this approach include simplicity in implementation, the ability to handle non-linear data, and resistance to overfitting, as it does not rely on a parametric model. However, it also has drawbacks, such as the need to store large volumes of data and the computation time required to calculate distances between instances during classification. In a broader context, Instance-Based Classification can be implemented using techniques like K-Nearest Neighbors (K-NN), which relies on the distance between points in a multidimensional space to determine the class of a new instance. This approach has gained popularity in various applications, from image classification to text analysis, due to its ability to adapt to different types of data and classification problems.
