Instance-based learning: What is it?

Alternatively referred to as Memory-based Learning, Instance-based Learning is a family of learning algorithms that compares newly encountered problem instances with training examples, hence avoiding explicit generalization. It is a crucial component of machine learning and is used to categorize fresh examples according to a similarity metric.

Features and Functionality

A method employed by instance-based learning models is using the instances in the learning process. The following are some of these models’ essential characteristics:

  • Classification: Based on the distance from the known examples, instance-based learning is highly effective in classifying unknown data.
  • Regression: By forecasting continuous outputs, these models are also capable of performing regression tasks.
  • Lazy Learning: One kind of lazy learning is instance-based learning, which uses training data during the testing phase rather than building generic models.

Advantages and Applications

Benefits of instance-based learning include the following:

  • Flexibility: Because it doesn’t rely on an earlier model, it can quickly adjust to changes.
  • Implementation simplicity: The algorithm is simple to use and comprehend.
  • No Training Phase: Since every instance represents itself, there is no need for a training phase.
  • Applications for instance-based learning can be found in computer vision, image identification, and recommendation systems, among other fields.


What is instance-based learning in machine learning? 

Instance-based learning, also known as memory-based learning, is a family of algorithms that classify new problem instances by comparing them with stored training examples. It avoids explicit generalization by relying on a similarity metric to make predictions.

What are the main features of instance-based learning models? 

Instance-based learning models use instances during the learning process. Key features include effective classification based on distance from known examples, the ability to perform regression tasks, and a lazy learning approach that utilizes training data during the testing phase without building a generalized model.

What are the advantages of instance-based learning? 

Advantages of instance-based learning include its flexibility to quickly adapt to changes since it doesn’t depend on a pre-built model, simplicity of implementation and understanding, and the absence of a training phase as each instance represents itself directly.

In what applications is instance-based learning commonly used? 

Instance-based learning is commonly applied in areas such as computer vision, image recognition, and recommendation systems, where its ability to classify and predict based on similarities to known examples is highly valuable.