What is the difference between supervised learning and unsupervised learning?

1. Supervised learning

Supervised learning is a machine learning method, which means that during the training process, the model uses labeled training data to learn the mapping relationship between input features and output labels. In supervised learning, the training data includes input features and corresponding output labels, and the goal of the model is to learn a function such that a given input feature can accurately predict the corresponding output label.

The basic process of supervised learning is as follows:

1. Data Collection: Collect labeled training data, which includes input features and corresponding output labels.

2. Feature extraction and selection: preprocessing, extraction, and selection of input features so that the model can better learn the relationship between features.

3. Choose a model: Choose an appropriate model based on the nature of the task, such as linear regression, decision tree, neural network, etc.

4. Define loss function: Define a loss function according to the characteristics of the problem to measure the error of the model on the predicted output.