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brief introduction Simply put: in the distance space, if most of the k nearest neighbors of a sample belong to a category, the sample also belongs to this category. API official website link api class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_par ...

data Jack cherish notes Machine learning practice kNN step 1 calculate the distance between the unknown point and the sample 2 distance increasing sort 3 select the first K samples according to the sorting results 4 determine the occurrence frequency of the first k samples 5 the category of the point with the highest frequency is the re ...

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 ...

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 ...

preface Decision tree is a common algorithm in machine learning. Relevant mathematical theories I also wrote in the column of mathematical modeling Mathematical modeling learning notes (XXV) decision tree Yes, this blog post does not pay attention to the relevant mathematical principles, but mainly focuses on the effect of using sklearn to re ...