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Question 79 - MLS-C01 discussion

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A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.

Which model should be used for categorizing new products using the provided dataset for training?

A.
An XGBoost model where the objective parameter is set to multi: softmax
Answers
A.
An XGBoost model where the objective parameter is set to multi: softmax
B.
A deep convolutional neural network (CNN) with a softmax activation function for the last layer
Answers
B.
A deep convolutional neural network (CNN) with a softmax activation function for the last layer
C.
A regression forest where the number of trees is set equal to the number of product categories
Answers
C.
A regression forest where the number of trees is set equal to the number of product categories
D.
A DeepAR forecasting model based on a recurrent neural network (RNN)
Answers
D.
A DeepAR forecasting model based on a recurrent neural network (RNN)
Suggested answer: A

Explanation:

XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions.References:

XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.

XGBoost Parameters: A reference guide for the parameters of XGBoost.

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asked 16/09/2024
Randy Kana
33 questions
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