Supervised learning and unsupervised learning are the two most basic categories in machine learning. The main difference between them is whether they need labeled data as training samples.
What is supervised learning and unsupervised learning?
Supervised learning refers to using labeled data (often called training data) as input to train a model to learn the relationship between input and output. After the model is learned, it can be used to predict the output of new, unseen data. The goal of this style of learning is to find a function that maps input data to output data. Examples of supervised learning include tasks such as classification, regression, sequence labeling, etc.
Unsupervised learning refers to learning a model from unlabeled data that tries to discover the structure and patterns inherent in the data, rather than predicting output variables. The goal of unsupervised learning is to discover similarities or associations between data and classify them into a class or extract features of the data. Examples of unsupervised learning include tasks such as clustering, dimensionality reduction, and anomaly detection.
Supervised learning and unsupervised learning have different application scenarios and functions:
- Supervised learning is usually applied when the output variable is known, such as classification and regression problems. Its goal is to predict new outputs by learning the relationship between known inputs and outputs.
- Unsupervised learning is suitable for unlabeled datasets, such as clustering and dimensionality reduction problems. Its goal is to discover useful patterns and structures from data for further analysis and processing.
In practical applications, supervised learning and unsupervised learning usually need to cooperate with each other, such as using unsupervised learning methods for data preprocessing and feature extraction, and then using supervised learning methods for classification or regression tasks.
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