[Pytroch] Data classification prediction based on support vector machine algorithm (Excel can directly replace data)

[Pytroch] Data classification prediction based on support vector machine algorithm (Excel can directly replace data)

1. Model Principle

Support Vector Machine (SVM) is a powerful supervised learning algorithm for binary and multiclass classification problems. Its main idea is to find an optimal hyperplane that can separate different classes of data points in the feature space.

The following are the basic steps and principles of using PyTorch to implement the support vector machine algorithm:

  1. Data Preparation: First, you need to prepare your training data. Each data point should have a feature (Feature) and a corresponding label (Label). Features are attributes used to describe data points, and labels are categories to which data points belong.

  2. Data preprocessing: According to the principle of SVM, data points need to be linearly separable. Therefore, you may need to do some data preprocessing, such as feature scaling or normalization, to ensure that the data is linearly separable.

  3. Defining the model: In PyTorch, you can define a SVM model as a linear model, e.g. usingnn.Line

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Origin blog.csdn.net/Gyangxixi/article/details/132262643