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Supervised learning is a type of learning where the training data is accompanied by labels indicating the class of the observations. What is the purpose of the labels?
Supervised learning is a type of learning where the training data is accompanied by labels indicating the class of the observations. What is the purpose of the labels?
- To predict the class of the observations (correct)
- To classify new data based on the training set
- To establish the existence of clusters in the data
- To measure the performance of the model
What is the main difference between supervised and unsupervised learning?
What is the main difference between supervised and unsupervised learning?
- Supervised learning requires labeled data, while unsupervised learning does not (correct)
- Supervised learning uses clustering algorithms, while unsupervised learning uses classification algorithms
- Supervised learning is more accurate than unsupervised learning
- Supervised learning is used for regression tasks, while unsupervised learning is used for classification tasks
What is the aim of unsupervised learning?
What is the aim of unsupervised learning?
- To predict the class of the observations
- To classify new data based on the training set
- To establish the existence of clusters in the data (correct)
- To measure the performance of the model
What is the process of classifying new data based on the training set called?
What is the process of classifying new data based on the training set called?
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Which algorithm is commonly used for classification based on decision trees?
Which algorithm is commonly used for classification based on decision trees?
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