You are creating an Azure Machine Learning experiment to predict the types of music your new customers will consume based on historical data about previous clients' music preferences. You need to…

The correct answer is D. Train Matchbox Recommender. You should use the Train Matchbox Recommender. The Train Matchbox Recommender is used to predict which items people are likely to choose based on historical data. The first step is to train the data, which pre-processes the data in preparation for the actual experiment. You…

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Question

You are creating an Azure Machine Learning experiment to predict the types of music your new customers will consume based on historical data about previous clients' music preferences. You need to pre-process the data. What should you use?

Options

ATrain Model
BTrain Clustering Model
CTrain Anomaly Detection Model
DTrain Matchbox Recommender

How the community answered

(38 responses)

A
8% (3)
B
3% (1)
C
5% (2)
D
84% (32)

Explanation

You should use the Train Matchbox Recommender. The Train Matchbox Recommender is used to predict which items people are likely to choose based on historical data. The first step is to train the data, which pre-processes the data in preparation for the actual experiment. You should not use the Train Anomaly Detection Model. This trainer sets the data up to detect exceptions to the norm in the data, which is exactly the opposite of predicting likely choices. You should not use the Train Clustering Model. This trainer prepares data to detect similar Items in a dataset, but it will not predict the items that are most liked. You should not use the Train Model. This is a generic trainer that prepares data for classification and regression analysis, but it will not predict the items that are most liked.

Topics

#Azure Machine Learning#recommender system#Matchbox Recommender#data preprocessing

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