Author: Zen and the Art of Computer Programming
In modern society, with the development of new technologies such as big data, cloud computing, and artificial intelligence, people pay more and more attention to the efficiency and accuracy of decision-making. More and more people are engaged in the field of intelligent decision-making, predicting future trends through data analysis, and making correct decisions based on this. Since the essence of intelligent decision-making is the collection of a series of decision-making rules, it involves advanced computer science, mathematics, statistics and other subject knowledge. Today, many companies have begun to engage in the research and development of intelligent decision-making systems. They collect massive amounts of data, use machine learning technology for analysis, develop intelligent decision-making models, and output decision results to users, ultimately improving work efficiency, reducing labor costs, and improving customer satisfaction. Some of these companies will also provide commercial services, such as providing intelligent decision support tools to help companies manage processes, optimize productivity, improve business strategies, and even convert decision results directly into products or services. But in any case, it is inseparable from how to apply machine learning technology to solve practical problems and achieve intelligent decision-making. But how can we truly understand and master machine learning algorithms? When should you use which algorithm? These questions are often asked, and the purpose of this article is to answer them.
2. Explanation of basic concepts and terms
2.1 Supervised learning and unsupervised learning
Machine learning (ML) is divided into two main methods, namely supervised learning (Supervised Learning) and unsupervised learning (Unsupervised Learning).
- In supervised learning, each data instance in the training set has a corresponding target value or category label, called a "label" or "class label." The purpose of supervised learning is to use this labeled information to classify unknown data. Common supervised learning algorithms include logistic regression, decision tree, support vector machine (SVM), naive Bayes method, etc.
- In unsupervised learning, there is no label information. This task often requires humans or other machine learning algorithms to discover the underlying structure of the data. Common