An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length, and sepal width. Which algorithm meets these requirements?

The correct answer is A. K-nearest neighbors (k-NN). Explanation K-nearest neighbors (k-NN) is correct because it is a supervised classification algorithm that predicts which category (class) a new data point belongs to by comparing it to its closest neighbors using multiple input features - perfectly suited for classifying…

Submitted by rania.sa· Mar 30, 2026Fundamentals of AI and ML

Question

Options

AK-nearest neighbors (k-NN)
BK-mean
CAutoregressive Integrated Moving Average (ARIMA)
DLinear regression

How the community answered

(28 responses)

A
89% (25)
C
7% (2)
D
4% (1)

Explanation

K-nearest neighbors (k-NN) is correct because it is a supervised classification algorithm that predicts which category (class) a new data point belongs to by comparing it to its closest neighbors using multiple input features - perfectly suited for classifying flower species (like Iris) based on four measurements. K-means (B) is wrong because it is an unsupervised clustering algorithm that groups unlabeled data, not one that predicts predefined classifications. ARIMA (C) is a time-series forecasting model used for sequential/temporal data (e.g., stock prices), making it completely irrelevant to flower classification. Linear regression (D) predicts a continuous numerical output (e.g., a price or temperature), not a discrete category label like a flower species.

Memory Tip: Think of k-NN as "asking your nearest neighbors what they are" - if the flowers around you are mostly roses, you're probably a rose. Whenever you see classification + labeled categories, think k-NN, not k-means (which has no labels).

Topics

#Classification#K-nearest neighbors#Supervised Learning#Machine Learning Algorithms

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