Which of these is a supervised learning algorithm?Answer choicesSelect an optionClassificationClusteringPCANone o

Break Down the Problem

1. Identify the types of learning algorithms given in the options.
2. Determine which option fits the definition of a supervised learning algorithm.

Relevant Concepts

1. Supervised Learning: This involves training a model on a labeled dataset, where the input data is paired with the correct output.
2. Classification: A type of supervised learning where the algorithm categorizes data into predefined classes.
3. Clustering: An unsupervised learning method that groups similar data points without pre-existing labels.
4. PCA (Principal Component Analysis): An unsupervised technique used for dimensionality reduction.
5. None: Implies no option fits the criteria.

Analysis and Detail

1. Classification: Involves predicting a discrete label (e.g., spam or not spam) based on input features. Hence, it is a supervised learning algorithm.
2. Clustering is not supervised because it does not use labeled data.
3. PCA is also not a supervised method as it seeks to reduce dimensions without labeled outputs.
4. None implies that none of the above options are supervised algorithms, which is incorrect since classification is supervised.

Verify and Summarize

Since classification is the only method that involves labeled output for education and training, it confirms its validity as a supervised learning algorithm.

Final Answer

Classification is a supervised learning algorithm.