What Instance-based Learning Meaning, Applications & Example
A learning algorithm that compares new instances to training data.
What is Instance-based Learning?
Instance-based learning is a type of machine learning where the model makes predictions based on the similarity of a new input to the training data. Instead of creating an explicit model during training, instance-based learning algorithms store the entire training set and compare new instances to the stored examples during prediction. The most commonly used algorithm for instance-based learning is k-Nearest Neighbors (k-NN).
Key Concepts of Instance-based Learning
- Memory-based Learning: In instance-based learning, the system retains the entire training dataset and uses it during prediction. There is no explicit model that generalizes from the data; instead, the system makes decisions based on stored instances.
- Similarity Measure: The performance of instance-based learning depends on how well the system can measure the similarity between instances. Common distance metrics include Euclidean distance, Manhattan distance, or cosine similarity.
- Lazy Learning: Instance-based learning is often called “lazy learning” because it doesn’t perform much computation during training. The heavy computation happens during prediction when the algorithm compares the input to the stored instances.
Methods of Instance-based Learning
- k-Nearest Neighbors (k-NN): In k-NN, a new instance is classified based on the majority class of the “k” closest instances in the training set. For regression tasks, the predicted value is the average of the nearest neighbors.
- Locally Weighted Learning: Instead of treating all instances equally, locally weighted learning assigns more importance to nearby instances when making predictions, allowing the model to be more sensitive to local patterns.
- Case-based Reasoning: A more general form of instance-based learning where the system recalls past cases and adapts the solutions to new but similar situations.
Applications of Instance-based Learning
- Pattern Recognition: Instance-based learning is used in tasks like handwriting recognition, facial recognition , and image classification where the model makes decisions based on the similarity between new inputs and previously seen examples.
- Recommender Systems: In content-based recommendation systems, instance-based learning can be used to recommend items based on user preferences similar to past interactions.
- Anomaly Detection: In anomaly detection , instance-based learning can help detect outliers by comparing new instances to known examples of normal behavior.
Example of Instance-based Learning
An example of instance-based learning is k-Nearest Neighbors (k-NN) for classifying animals based on their features like weight and height. When a new animal with unknown classification is observed, the k-NN algorithm finds the “k” closest animals in the training set and assigns the new animal the most common classification among those neighbors. If the k closest neighbors are mostly “mammals,” the algorithm classifies the new animal as a mammal.