Comparing Model-Based and Instance-Based Learning in Machine Learning

An exploration into the distinct approaches of model-based and instance-based learning within the field of machine learning.

• Model-Based Learning
  This category encompasses supervised learning models (supervised ML) that leverage training data to build a decision-making framework. Once this framework is developed, the initial training data can be set aside.

  Examples include the decision tree and SVM (Support Vector Machine) algorithms, among others in model-based learning.

• Instance-Based Learning
  This approach utilizes a dataset as the very foundation of its model. Here, the dataset is essential and cannot be discarded, as it forms the core of the decision-making process.

  A prime example is the kNN (k-Nearest Neighbours) algorithm, which processes new input by evaluating its similarity or dissimilarity to existing data points.