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feature selection Reason for feature selection Redundancy: some features have high correlation and are easy to consume computing performance Noise: some features have a negative impact on the prediction results What is feature selection Feature selection is simply to select some features from all the extracted features as the features o ...

Reprinted from: https://blog.csdn.net/kgzhang/article/details/77479737 torch.optim is a package that implements a variety of optimization algorithms. Most general methods have been supported and provide rich interface calls. More refined optimization algorithms will be integrated in the future. In order to use torch.optim, we ...

[source code analysis] how PyTorch implements backward propagation (4) -- specific algorithm catalogue[source code analysis] how PyTorch implements backward propagation (4) -- specific algorithm0x00 summary0x01 worker thread body1.1 thread body code1.2 using Ready Queue0x02 reverse calculation of overall logic0x03 preparation0x04 core logic0x05 ...

Decision Tree Picks Out Good Watermelons 1. ID3 Algorithmic Theory (1) algorithm core The core of the ID3 algorithm is to select the partitioned features based on the information gain and then build the decision tree recursively (2) Feature selection Feature selection means selecting the optimal partition attribute and selecting a feat ...

1, Decision tree 1. Concept Decision tree is a classification algorithm based on tree structure. We hope to learn a model (i.e. decision tree) from a given training data set and use the model to classify new samples. The decision tree can intuitively show the classification process and results. Once the model is built successfully, the cl ...

1, Multiple linear regression In regression analysis, if there are two or more independent variables, it is called multiple regression. In fact, a phenomenon is often associated with multiple factors. It is more effective and practical to predict or estimate the dependent variable by the optimal combination of multiple independent variab ...

  catalogue Construction of decision tree General process of decision tree information gain   Write code to calculate empirical entropy Calculate information gain using code Partition dataset Select the best data set division method   Information gain rate Gini coefficient   Differences between ID3, C4.5 and CART I ...

The purpose of regression is to predict the numerical target value. A sentence input to write a target value calculation formula is the so-called regression equation, and the process of calculating the regression coefficient is regression. Once you have these regression coefficients, it's very easy to make predictions at a given input. The spec ...

Image feature vector extraction and application of transfer learning This article will discuss the concept of transfer learning in computer vision, an ability to use a pre trained model to learn from data sets other than its previously trained data sets. For example: There are two different data sets A and B, and our task is to identify diff ...

introduction Linear regression may be the earliest machine learning algorithm we came into contact with. In the high school mathematics textbook, we officially knew this friend for the first time. We obtained the linear regression equation of the data through the least square method, and then obtained the parameters of the model. But in ...