The Instance-Based Learning Quiz: Are You an Expert?

Instance-based learning is a type of learning that is based on specific instances or examples rather than generalized rules.

K-Nearest Neighbors (KNN) is a type of instance-based learning where the classification of a point is based on the points that are nearest to it.

KNN is a non-parametric algorithm because it does not assume any specific form or underlying structure in the data. Instead, it relies on a measure of similarity between each pair of data points.

The k value in the k-NN algorithm defines how many neighbors will be checked to determine the classification of a specific query point.

The KNN algorithm uses a majority voting mechanism. It collects data from a training data set, and uses this data later to make predictions for new records. For each new record, the k-closest records of the training data set are determined.

In K-Nearest Neighbors (KNN), larger values of K generally result in smoother decision boundaries, reducing the chance of overfitting.

One main advantage of instance-based learning is that it doesn't require a training phase as it uses the training instances for prediction.

The main disadvantage of instance-based learning is that it has high computational cost as it needs to compute the distance to all training points.

This distance is the most widely used one as it is the default metric that SKlearn library of Python uses for K-Nearest Neighbour. It is a measure of the true straight line distance between two points in Euclidean space.

KNN can be used for Recommendation Systems. Although in the real world, more sophisticated algorithms are used for the recommendation system. KNN is not suitable for high dimensional data, but KNN is an excellent baseline approach for the systems.