Machine learning is undoubtedly a hot topic in the current data analysis field. Its theories and methods have been widely used to solve complex problems in engineering applications. Many people use machine learning algorithms more or less in their daily work. In the field of machine learning, no algorithm can solve all problems perfectly. For example, neural networks are not in every situation superior to decision trees, and vice versa. They are affected by many factors, such as the size or structure of your dataset.
As a result, when evaluating mobile number list performance and picking an algorithm on a given test set, you should use a different algorithm based on the specific problem. For example, if the model requires strong interpretability, the first thing that comes to mind is logistic (linear) regression. If the model requires high accuracy and fast speed, the first thing that comes to mind is . If the amount of data is huge and sparse, what is the first thing that comes to mind? Solve this problem with a neural network.
Therefore, how to choose a machine learning algorithm, which algorithm to choose, and what issues to pay attention to when modeling algorithms have become a difficult problem for engineers. The purpose of this article summarizes the advantages and disadvantages of commonly used machine learning algorithms for your reference in work, study and even interviews . Machine learning is mainly divided into supervised learning and unsupervised learning. This article sorts out these two aspects.