What should the data science team do to meet these requirements?

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A company is building a new supervised classification model in an AWS environment.The company’s data science team notices that the dataset has a large quantity of variables.All the variables are numeric.The model accuracy for training and validation is low.The model’s processing time is affected by high latency.The data science team needs to increase the accuracy of the model and decrease the processing time.

What should the data science team do to meet these requirements?

Create new features and interaction variables.

Use a principal component analysis (PCA) model.

Apply normalization on the feature set.

Use a multiple correspondence analysis (MCA) model.

Explanations:

Creating new features and interaction variables might improve accuracy, but it is unlikely to reduce processing time or directly address the issue of high dimensionality.

Using Principal Component Analysis (PCA) reduces dimensionality by transforming features into principal components, which can help improve accuracy by removing noise and decrease processing time by reducing the number of variables.

Normalization can improve model performance in some cases, but it won’t directly address the issue of high dimensionality or reduce processing time.

Multiple Correspondence Analysis (MCA) is used for categorical data, not numeric data, so it wouldn’t be suitable for this scenario.

2025-10-04

1 Comment

  1. Joshua
    Author

    I organize that the answer is:
    Use a principal component analysis (PCA) model.

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