Topic 1 Question 276
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.
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B. Use a principal component analysis (PCA) model. This is because PCA can help to reduce the number of variables while preserving the most important information, which can help to improve the accuracy of the model and reduce the processing time.
👍 2usamazubairi2023/12/14- 正解だと思う選択肢: B
need to reduce the dimension of features in order to enhance accuracy on train and test data since # of features are huge.
👍 1xiaoeason2023/12/15 - 正解だと思う選択肢: B
It's PCA. It's not MCA because all the values are numeric and not categorical.
👍 1taustin22023/12/23
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