Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model’s performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.

Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model’s performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.

Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets. MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.

Option C is incorrect because setting the predictor type to regressor would change the model’s objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.

Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.

Amazon SageMaker Linear Learner Algorithm

Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm

[Principal Component Analysis - Scikit-learn]

[Multidimensional Scaling - Scikit-learn]