Machine Learning 101 Practice Test: K-Nearest Neighbor Algorithm and Nearest Neighbor Analysis — Flashcards

The K-Nearest Neighbor (KNN) algorithm is a supervised machine learning algorithm that classifies and predicts data points. It's used in many machine learning applications, including recommendation engines, concept search, and pattern recognition. 

Here are some steps for the KNN algorithm:
Assign a value to K.
Calculate the distance between the new data entry and all other existing data entries.
Arrange the distances in ascending order.
Find the K nearest neighbors to the new entry based on the calculated distances.
Assign the class that is most common among the K nearest neighbors. 
 

The value of K is crucial in the KNN algorithm. It should be chosen based on the input data. If the input data has more outliers or noise, a higher value of K would be better. It is recommended to choose an odd value for K to avoid ties in classification. 

Nearest Neighbor Analysis is a method for classifying cases based on their similarity to other cases. In machine learning, it was developed as a way to recognize patterns of data without requiring an exact match to any stored patterns.