You are a machine learning specialist working for a government agency that uses a series of web application forms to gather citizen data for census purposes. You have been tasked with finding novel user entries as they are entered by your citizens. A novel user entry is defined as an outlier compared to the established set of citizen entries in your datastore.
You have cleaned your citizen datastore to remove any existing outliers. You now need to build a model to determine whether new entries on your web application are novel. Which algorithm best fits these requirements?

Answer options:

A.Multinomial Naive Bayes
B.Bernoulli Naive Bayes
C.Principal Component Analysis
D.Support Vector Machine

Answer: D
Option A is incorrect. The Multinomial Naive Bayes algorithm is best suited for classification tasks where you wish to know the frequency of a given observation. You are trying to determine whether you have a novel observation.
Option B is incorrect. The Bernoulli Naive Bayes algorithm is used in classification tasks where you wish to know whether a known class appears in your observation. You are trying to determine whether you have a novel observation.
Option C is incorrect. The Principal Component Analysis algorithm is used to reduce feature dimensionality. You are trying to determine whether you have a novel observation.
Option D is correct. The Support Vector Machine algorithm can be used when your training data has no outliers, and you want to detect whether a new observation is a novel entry.
Reference:
Please see the SciKit Learn page titled 1.4. Support Vector Machines (https://scikit-learn.org/stable/modules/svm.html), the SciKit Learn page titled 2.7. Novelty and Outlier Detection (https://scikit-learn.org/stable/modules/outlier_detection.html#outlier-detection), and the Amazon SageMaker developer guide titled Principal Component Analysis (PCA) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html)