The correct answer is: Supervised Learning.
Supervised learning is a type of machine learning in which a model is trained on labeled data. This means that the model is given a set of data that includes both the input data and the desired output. The model then learns to map the input data to the output data.
Supervised learning is often used for tasks such as classification and regression. In classification, the model is trained to assign labels to data points. For example, a model might be trained to classify images of cats and dogs. In regression, the model is trained to predict a continuous value. For example, a model might be trained to predict the price of a house.
Supervised learning is a powerful tool that can be used to solve a variety of problems. However, it is important to note that supervised learning requires labeled data. This can be a difficult and time-consuming task.
Here is a brief explanation of each option:
- Regression is a type of supervised learning in which the model is trained to predict a continuous value. For example, a model might be trained to predict the price of a house.
- Clustering is a type of unsupervised learning in which the model is used to find groups of similar data points. For example, a model might be used to find groups of customers who have similar purchasing habits.
- Classification is a type of supervised learning in which the model is trained to assign labels to data points. For example, a model might be trained to classify images of cats and dogs.
I hope this helps!