Generative AI Certificate Q&A: How K Nearest Neighbor like the old saying, “birds of a feather flock together?”

Question

How is K Nearest Neighbor like the old saying, “birds of a feather flock together?”

A. Multiclass classification is like a flock of birds that needs to be classified.
B. Classify unknown data against the closest data that you do know.
C. You want to fly through the data as quickly as possible.
D. Make sure you know everything about the data before you try to classify.

Answer

B. Classify unknown data against the closest data that you do know.

Explanation

The correct answer is B. Classify unknown data against the closest data that you do know.

The K Nearest Neighbor (KNN) algorithm is a non-parametric, supervised learning method that is used for classification and regression problems. It is based on the idea that similar data points tend to be close to each other in a feature space, and that the label or value of an unknown data point can be predicted by looking at its nearest neighbors

The KNN algorithm works as follows:

• Given a set of labeled training data, a distance metric (such as Euclidean distance), and a positive integer k, the algorithm stores the training data in memory.
• Given an unlabeled query or test data point, the algorithm finds the k closest training data points to it, based on the distance metric.
• For classification problems, the algorithm assigns the query data point the most common label among its k nearest neighbors, based on a majority vote. For regression problems, the algorithm assigns the query data point the average value of its k nearest neighbors.

The KNN algorithm is like the old saying, “birds of a feather flock together”, because it assumes that data points that are similar to each other (belong to the same class or have similar values) are likely to be close to each other in a feature space, and that data points that are dissimilar to each other (belong to different classes or have different values) are likely to be far apart from each other. By using this assumption, the algorithm can classify unknown data against the closest data that it does know.

The other options are incorrect because they do not describe how the KNN algorithm works.

• A. It’s a way for computer scientists to optimize server code in a hosted reason repository. This option has nothing to do with the KNN algorithm or the old saying. A reason repository is a database or platform that stores and manages rules and facts for reasoning engines, which are different from KNN algorithms.
• C. You want to fly through the data as quickly as possible. This option does not explain how the KNN algorithm works or how it relates to the old saying. The KNN algorithm does not necessarily fly through the data quickly, as it requires storing all the training data in memory and computing distances for every query data point.
• D. Make sure you know everything about the data before you try to classify. This option is also irrelevant to the KNN algorithm or the old saying. The KNN algorithm does not require knowing everything about the data before classifying, as it only uses local information from the nearest neighbors. However, some preprocessing steps such as normalization or feature selection may be helpful to improve the performance of the algorithm.

Reference

• k-nearest neighbors algorithm – Wikipedia
• K-Nearest Neighbor(KNN) Algorithm for Machine Learning – Javatpoint
• What Is K-Nearest Neighbor? An ML Algorithm to Classify Data (g2.com)
• What is the k-nearest neighbors algorithm? | IBM
• k-Nearest Neighbor Classification | SpringerLink
• K-Nearest Neighbor(KNN) Algorithm – GeeksforGeeks

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