Which of the following is a common application of supervised machine learning?Unsupervised clustering of customer dataPredicting the win rate of a team from historical dataAll of the aboveComparing variances of two populations
Question
Which of the following is a common application of supervised machine learning?Unsupervised clustering of customer dataPredicting the win rate of a team from historical dataAll of the aboveComparing variances of two populations
Solution
The common application of supervised machine learning from the options given is "Predicting the win rate of a team from historical data".
Here's why:
Supervised machine learning is a type of machine learning where the model is trained on a labelled dataset. That is, the model is provided with input data along with the correct output. It uses this training data to learn the relationship between the input and the output, and once the model is trained, it can be used to predict the output for new, unseen input data.
In the context of the options provided:
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Unsupervised clustering of customer data: This is an application of unsupervised learning, not supervised learning. In unsupervised learning, the model is not provided with the correct output in the training data. Instead, it finds patterns and structures in the input data on its own. Clustering is a common unsupervised learning technique where the model groups similar data together.
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Predicting the win rate of a team from historical data: This is an application of supervised learning. The model can be trained on historical data where the input is the features of the team and the games, and the output is whether the team won or not. Once trained, the model can predict the win rate for new games.
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All of the above: Since the first option is not an application of supervised learning, this option is incorrect.
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Comparing variances of two populations: This is a statistical analysis task, not typically an application of supervised machine learning.
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