Supervised Learning

Supervised learning is a method of training models using labeled datasets, and its main steps include:

Data collection and preprocessing: Collect and preprocess data for model training.

Feature selection: Select features related to the problem based on the characteristics of the data.

Model selection: Choose the appropriate machine learning algorithm for training.

Model training: Train the model using a training dataset for easy prediction.

Model evaluation: Use a test dataset to evaluate the performance of the model and make adjustments.

Model deployment: Deploy the trained model to the production environment for practical application.

Unsupervised Learning

Unsupervised learning is a method of training models using unlabeled datasets, and its main steps include:

Data collection and preprocessing: Collect and preprocess data for model training.

Feature selection: Select features related to the problem based on the characteristics of the data.

Model selection: Choose an appropriate unsupervised learning algorithm for training.

Model training: Train the model using a training dataset for classification, clustering, and other operations.

Model evaluation: Use a test dataset to evaluate the performance of the model and make adjustments.

Model deployment: Deploy the trained model to the production environment for practical application.

Reinforcement Learning

Reinforcement learning is a method of learning how to make optimal decisions in different states by taking actions and receiving rewards in the environment. Its main steps include:

Environmental model: Establish an environmental model to enable the model to understand the state and rules of the environment.

Action selection: Select appropriate actions based on the environmental model.

Reward evaluation: Evaluate rewards based on the results of actions, in order for the model to learn how to make optimal decisions.

Model training: Train the model using a training dataset for easy prediction.

Model evaluation: Use a test dataset to evaluate the performance of the model and make adjustments.

Model deployment: Deploy the trained model to the production environment for practical application.

Author: OpenChat

Link: https://juejin.cn/post/7317133892708335653

Source: Rare Earth Gold Mining

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