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Catalog 1. Overview of the process (this article is mainly the red box part) 2. Feature Processing Section 1. Overview 1.1 Some common questions 2. Specific treatment (see red box above for specific order) 2.1 Missing value handling (delete or fill) 2.2 Data format processing (data set partition, time format, etc.) 2.3 Data sampling (ov ...

1. WSN Model 1.1 Motivation In recent years, with the development of distributed environment such as peer-to-peer network, cloud computing and grid computing, wireless sensor network (WSN) has been widely used.Is a new computing and network mode that can be defined as a network of tiny, small, expensive and highly intelligent devices called s ...

Ideas and steps of kNN algorithm The basic idea of kNN algorithm is as follows: Suppose there are n samples in the sample set, which have discrimination on m features, and they belong to one of 0 or 1 respectively. Suppose the eigenvector of the ith sample is ( a ...

Author CSDN: Sisyphus of the attack Link to this article: https://blog.csdn.net/qq_42216093/article/details/116994199 Copyright notice: This article is the author's original article. Reprint requires the consent of the author Nowadays, machine learning is hot, and Python is the most commonly used implementation of machine learning for ...

20210911 0. Introduction In most cases, machine learning experiments are divided directly by train test. Generally speaking, this method has little impact on the data set with relatively small data set, but it is biased for the data set with relatively small data set. (I remember it was said in a book, and it was also mentioned in the course ...

First question subject 1 You train a learning algorithm, and find that it has unacceptably high error on the test set. You plot the learning curve, and obtain the figure below. Is the algorithm suffering from high bias, high variance, or neither? answer: C Resolution: It's obvious from this figure that no amount of data is useful. ...

preface: This column mainly combines OpenCV4 to realize some basic image processing operations, classical machine learning algorithms (such as K-Means, KNN, SVM, decision tree, Bayesian classifier, etc.), and common deep learning algorithms. Series of articles, continuously updated: OpenCV4 machine learning (I): Construction and configuratio ...

A garbage sorting project takes you to play with the paddle (2) Continued above< A garbage sorting project takes you to play with the paddle (1) >: Based on PaddleClas Realize garbage classification, export the information model, use PaddleHub Serving for service deployment, and use PYQT5 to realize visualization. This paper takes waste ...

summary From today on, we will start a journey of natural language processing (NLP). NLP can let us process, understand and use human language to realize the communication bridge between machine language and human language key word Keywords, i.e. key words, can describe the essence of an article and have important applications in docum ...

1. General 1.1 how does the decision tree work? Decision Tree is a nonparametric supervised learning method. It can summarize decision rules from a series of characteristic and labeled data, and present these rules with the structure of tree view to solve the problems of classification and regression. Decision Tree algorithm is easy to unders ...