Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 103 discussion

k-NN: This is a classification algorithm that predicts the class of a new data point by comparing it to the closest data points in the training set. In this case, the new data point would be a new flower with measured petal and sepal dimensions, and the algorithm would find the flowers in the training set that are most similar to it based on these features.

The practitioner wants to classify flowers based on measurements. This indicates a classification problem. A. K-nearest neighbors (k-NN): This is a classification algorithm that classifies data points based on the majority class among their k-nearest neighbors. It's suitable for this scenario. B. K-means: This is a clustering algorithm used for unsupervised learning. It groups data points into clusters based on similarity, but it doesn't perform classification with predefined labels. C. Autoregressive Integrated Moving Average (ARIMA): This is a time series forecasting model used for predicting future values based on past data trends. It's not suitable for classification based on static measurements like flower dimensions. D. Linear regression: This is a regression algorithm used for predicting continuous values. It's not suitable for classification into discrete categories like flower types. Therefore, A. K-nearest neighbors (k-NN) is the appropriate algorithm for this classification task

The correct answer is A. K-nearest neighbors (k-NN) is a classification algorithm suitable for predicting the classification of flowers based on the provided features.

For a classification task where the goal is to predict the type of flower based on several features, K-nearest neighbors (k-NN) is the most appropriate algorithm.

K-nearest neighbors (k-NN) is a supervised learning algorithm commonly used for classification tasks. It works by finding the "k" closest data points (neighbors) to a given input and assigning the class based on majority voting among these neighbors. In this case, the AI practitioner wants to classify flowers based on features like petal length, petal width, sepal length, and sepal width, making k-NN a suitable algorithm.