You work as a machine learning specialist for a video game software company. You have been asked to produce a machine learning model that predicts whether a newly released game will eventually become a successful product that earns the company profits. Your data used for your model is product information and product ratings from social media. Your management team would like to use your model results to help them decide if a new game is worth investing in marketing dollars to promote the game further. Which model and objective will best match your model requirements?

Answer options:

A.XGboost, multi-softmax
B.XGboost, binary:logistic
C.DeepAR, reg:logistic
D.Random Cut Forest, binary:logistic

Correct Answer: B
Option A is incorrect. XGBoost is a good choice for your algorithm, but the multi-softmax objective is used for multiclass classification. You are trying to predict whether your newly released game will eventually succeed in making your company money or not; a binary or logistic regression problem.
Option B is correct. XGBoost is a good choice for your algorithm, and the binary:logistic objective is the correct objective since it is used for binary classification problems. You are trying to predict whether your newly released game will eventually succeed in making your company money or not; a binary or logistic regression problem.
Option C is incorrect. The DeepAR algorithm is not the correct choice for your algorithm. The DeepAR algorithm is used with time-series data. You are using product information and product ratings from social media. Also, there is no reg:logistic objective for the DeepAR algorithm.
Option D is incorrect. The Random Cut Forest algorithm is an unsupervised algorithm used to detect anomalous data points in a data set. You would not try to use the Random Cut Forest algorithm to solve a logistic regression problem like predicting whether your newly released game will eventually succeed in making your company money or not.
References:
Please see the AWS Amazon SageMaker Examples jupyter notebook titled Predicting Product Success When Review Data Is Available (https://github.com/aws/amazon-sagemaker-examples/blob/master/introduction_to_applying_machine_learning/video_game_sales/video-game-sales-xgboost.ipynb),
The Amazon SageMaker developer guide page titled XGBoost Hyperparameters (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html),
The Amazon SageMaker developer guide page titled Random Cut Forest (RCF) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html),
The Amazon SageMaker developer guide page titled DeepAR Forecasting Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html),
The Amazon SageMaker GitHub repository titled XGBoost Parameters (https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst#learning-task-parameters),
The Wikipedia page titled Logistic regression (https://en.wikipedia.org/wiki/Logistic_regression#:~:text=Logistic%20regression%20is%20a%20statistical,a%20form%20of%20binary%20regression)