Examtopics

コメント(5)

• 正解だと思う選択肢: A

  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

  👍 4

  Moon2024/12/31
• 正解だと思う選択肢: A

  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.

  👍 1

  ap64912024/12/26
• 正解だと思う選択肢: A

  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.

  👍 1

  aws_Tamilan2024/12/27