Topic 1 Question 199
2 つ選択A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2,000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.
Which changes in model training would MOST likely improve the model's F1 score?
Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-learn multi-dimensional scaling (MDS) algorithm.
Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.
Use the SageMaker k-means algorithm with k of less than 1,000 to train the model.
Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.
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コメント(4)
- 正解だと思う選択肢: AE
KNN can be used for dimensionality reduction through NCA (https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#)
👍 8Peeking2022/12/10 - 正解だと思う選択肢: AE
A. Correct B. Incorrect. MDS is Non-linear dimensionality reduction method. https://towardsdatascience.com/11-dimensionality-reduction-techniques-you-should-know-in-2021-dcb9500d388b C. Incorrect. This is a classification problem instead of Regression. D. Incorrect. K-means is for Clustering(Unsupervised learning). E. Correct.
👍 5Jerry842023/01/12 - 正解だと思う選択肢: AE👍 4Mllb2023/04/02
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