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NEW QUESTION 32
An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers’ current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.
Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?
- A. The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.
- B. The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.
- C. The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.
- D. The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.
Answer: B
NEW QUESTION 33
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?
- A. Root Mean Square Error (RMSE)
- B. Confusion matrix
- C. Area under the curve
- D. Residual plots
Answer: D
NEW QUESTION 34
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users.
What should the Specialist do to meet this objective?
- A. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- B. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
- C. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- D. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
Answer: C
Explanation:
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
NEW QUESTION 35
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